P01-01

Cyclosporin A: Conquering Conformational Complexity
and Chameleonicity
Satoshi Ono 1

Matthew R. Naylo r2

Chad E. Townsend 2

Ono.satoshi@mg.mt-pharma.co.jp

Chieko Okumura 1

Okimasa Okada 1

Hsiau-Wei Lee 2

R. Scott Lokey 2
1

2

Discovery Technology Laboratories, Innovative Research Division, Mitsubishi Tanabe Pharma
Corporation, 1000, Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa 227-0033, Japan
Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz,
California 95064, United States

Keywords: Cyclic peptide, Conformation, bRo5 compound, Multicanonical MD
The chameleonic behavior of cyclosporin A (CsA) was investigated through conformational
ensembles employing multicanonical molecular dynamics simulations that could sample the wide
range of conformational space including cis and trans isomers of N-methylated amino acids. These
assessments were conducted in various explicit solvents ranging polar to apolar using AMBER ff03,
AMBER10:EHT, AMBER12:EHT, and AMBER14:EHT force fields. The conformational details
were discussed employing the free-energy landscapes (FELs) at T = 300 K. It was observed that theexperimentally determined structures of CsA were only a part of the conformational space.
Comparing the ROESY measurements in cyclohexane-d12 and hexane-d14, the major conformationsin those apolar solvents were essentially the same as that in CDCl 3 except for the observation ofsome sidechain rotamers. The effects of the metal ions on the conformations, including the cis/transisomerization, were also investigated. Based on the analysis of FELs, it was concluded that the
AMBER ff03 force field best described the experimentally derived conformations, indicating thatCsA intrinsically formed membrane-permeable conformations and that the metal ions might be key
to the cis/trans isomerization of N-methylated amino acid before binding a partner protein .
[1] Ono, S.; Naylor, M. R.; Townsend, C. E.; Okumura, C.; Okada, O.; Lee, H. -W.; Lokey, R. S.
Cyclosporin A: Conformational Complexity and Chameleonicity. J. Chem. Inf. Model. 2021, 61,
5601 – 5613.

P01-02

Toward a in silico screening of broad-spectrum
antibody that is effective against wide variety ofvariants
Kanji Oshima1

Kanji.Oshima@kaneka.co.jp

Shinichi Yoshida1

Shinichi.Yoshida@kaneka.co.jp

Hiroshi Kita1
Hiroshi.Kita@kaneka.co.jp
1

KANEKA CORPORATION 1-8, Miyamae-cho, Takasago-cho, Takasago Hyogo 676-8688
Japan

Keywords:broad spectrum antibody, In silico screening, mutation, fluctuation
Influenza viruses and novel coronaviruses such as SARS-CoV-2 are prone to frequent
mutations of the viral genome, resulting in mutations in the amino acid sequence of the surface
protein of the virus that can be recognized as an antigen (epitope site of neutralizing antibodies),changing its antigenicity. As a result, antibodies that have neutralizing ability in the wild type
become less effective in variants. Therefore, attention is being paid to the development ofbroad-spectrum neutralizing antibodies that are less susceptible to viral mutations. In the study,
we have focused on viral genome mutations and investigated a method of predicting epitopesites where mutations will seldom occur in the future.
We have obtained the amino acid sequence data of human influenza A hemagglutinin (HA)
from the NCBI influenza-related protein sequence database (Influenza Virus Resources) andperformed sequence analysis (total 45,545 sequences, unique sequence after removing
duplicated sequences ~14.173 sequences), We have found that many amino acid residues (560
out of 565 residues in total length) accumulated mutations during the period from 1918 to about
100 years. Next, we have performed MD (Molecular Dynamics) analysis using the oldest HA
sequence structure in 1918 (PDBID:3gbn), and calculated the fluctuation of each amino acidresidue (rmsf). As a result of examining the relationship between fluctuation and mutation
accumulation rate, we have found that the mutation accumulation rate tended to decrease as thefluctuation became smaller. Furthermore, in a known broad-spectrum neutralizing antibody for
HA (CR6261), we have confirmed that most of the amino acid residues of the epitope arecharacterized by small fluctuations, small mutation accumulation rates, and exposure to
antibody-accessible surfaces.
These results suggest that if the amino acid residues on the antibody-accessible surface havesmall fluctuations, they will be less likely to be mutated in the future, then neutralizing
antibodies with these amino acid residues as epitopes become effective therapeutic agents(broad-spectrum antibodies) against various mutant strains in the long term.

P01-03

Structure prediction of cyclic peptides in solventwith integrating multiple technologies
Kentaro Takai

Yoshiaki Tanida

Toshio Manabetakai.kentaro@fujitsu.com

tanida.yoshiaki@fujitsu.com

tmanabe@fujitsu.com

Chieko Terashima

Mitsuhito Wada

Takashi Mitsui

Hiroyuki Satoc.terashima@fujitsu.com

mwada@fujitsu.com

tmitsui@fujitsu.com

shryk@fujitsu.com

Fujitsu Ltd, 1-5 Omiya-cho, Saiwai-ku, Kawasaki-shi, Kanagawa, 212-0014, Japan

Keywords: structure prediction, cyclic peptides
Drug discovery in middle sized molecules such as cyclic peptides is an attracting area because
they have higher selectivity to target proteins and lower toxicity compared to small molecules andhigher membrane permeability and lower production cost compared to antibodies. Although theseproperties depend on their structures, the standard technology to analyze their large conformational
space has not yet been established. Thus, we developed a method to predict three-dimensional
structures of cyclic peptides in solvent.
Our proposed method consists of multiple technologies. The procedure is as follows (Figure 1).
(i) We prepared a coarse-grained model for a sequence of an input peptide, where one amino acid isrepresented as one particle [1]. (ii) We explored the stable configuration in this model by Fujitsu
Quantum-inspired Computing Digital Annealer [2]. The interaction potentials between particles are
predicted by AI. (iii) We transformed the coarse -grained model to the all-atom model using themonomer structures in DB. (iv) We prepared input files for REST2 simulation [3]. Here, we used
our original force field (FF-FOM) [4] and the RESP charges of monomers which are registered in
DB. (v) We performed REST2 simulations on high -performance computing. (vi) We extracted
representative structures by density peak clustering [5].
We predicted the stable structure of Cyclosporin A in the chloroform solvent and found that thecluster center structure in most frequent cluster was similar to the experimentally observed one(RMSD of the main chain and CB atoms is 0.87 Å). Although expert knowledge is required to use
above each technology, we automated our method so that anyone can use it easily.
Our method achieved the proposal of a few stable structures of cyclic peptides in solvent. Aiming
to improve accuracy and processing speed, we are energetically developing our technolog ies.

Figure 1: The procedure of our method.
[1] A. Godzik, A. Kolinski, and J. Skolnick. J. Comput. Chem., 14, 10, 1194 (1993).
[2] https://www.fujitsu.com/global/services/business-services/digital-annealer/[3] L. Wang, R. A. Friesner, and B. J. Berne, J. Phys. Chem. B, 115, 9431 (2011).
[4] H. Fujitani, A. Matsuura, S. Sakai, H. Sato, and Y. Tanida, J. Chem. Theory Comput. 5, 1155 (2009).
[5] A. Rodriguez, A. Laio, Science 344, 6191, 1492 (2014).

P01-04

Molecular dynamics study of the interaction between
a GST dimer and a novel peptidic covalent aptamer

1

2

3

4

Lisa Matsukura 1

Naoyuki Miyashita 1,2

liz@miyashita-lab.net

miya@waka.kindai.ac.jp

Masumi Taki 2

Shinichi Watanabe 2

taki@pc.uec.ac.jp

shin.watanabe@uec.ac.jp

Jay Yang 3,4
jyang75@wisc.edu

Department of Biological System Engineering, Graduate school of Biological Oriented Scienceand Technologies, KINDAI University, 930 Nishimitani, Kinokawa, Wakayama 649−6493,
Japan
Department of Engineering Science, Graduate School of Informatics and Engineering,
University of Electro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo 182-8585, Japan
Department of Anesthesiology, University of Wisconsin, School of Medicine and Public
Health, Madison, Wisconsin 53706, USA
Department of GI Surgery II, Hokkaido Univ Grad Sch Medicine, Sapporo, Hokkaido, Japan

Keywords: Molecular dynamics Simulation, Ligand Docking
Recently, peptidic covalent aptamer (PecA)[1] has been reported by Tabuchi et. al., as a novel
peptide drug for Glutathione S-transferase (GST). It specifically binds to the dimer structure of GST.
PecA consists of a peptide, which has seven amino acids (LESCAWY), and a compound, arylfluorosulfate (aryl- OSO2F; AFS), which connects to the cysteine in the peptide. A previous study
suggested that PecA binds around the glutathione binding site of GST dimer, located in thehydrophobic regions. In this system, TYR111 in GST dimer and the fluorine of AFS in PecA are
covalently bound. However, the detailed binding pose has not been clarified experimentally. In thisstudy, we investigated the binding pose of PecA on GST and the drug-bound state using moleculardocking and molecular dynamics simulations.

Fig. 1 The structure of a) GST dimer, b) the complex structure of GST dimer and PecA.

[1] Tabuchi, S.; Watanabe, T.; Katsuki, R.; Ito, Y.; Taki, M., Direct screening of a target-specificcovalent binder: stringent regulation of warhead reactivity in a matchmaking
environment, Chemical Communications , 2021 , 57 , 5378-5381.

P01-05

QM/MM simulations of artificial ion channel
in membrane-water system
Mayuko Nakagawa 1

Toru Ekimoto 1

Tsutomu Yamane 2

w215421d@yokohama-cu.ac.jp

ekimoto@yokohama-cu.ac.jp

tsutomu.yamane@riken.jp

Kohei Sato 3

Kazushi Kinbara 3

Mitsunori Ikeguchi 1,2

koheisato@bio.titech.ac.jp

kinbara.k.aa@m.titech.ac.jp

ike@yokohama-cu.ac.jp

1

2
3

Yokohama City Univ., 1-7-29 Suehiro-chou, Tsurumi-ku, Yokohama-shi, Kanagawa, 230-0045,
Japan
RIKEN, 1-7-22 Suehiro-chou, Tsurumi-ku, Yokohama-shi, Kanagawa, 230-0045, JapanTokyo Inst. of Tech., 4259 Nagatsuda-chou, Midori-ku, Tokyo, 226-8503, Japan

Keywords: Artificial ion channel, Ion selectivity, QM/MM
The development of artificial molecular machines mimicking biomolecular functions is
progressing. Recently, inspired by potassium channels found in nature, a cyclic artificial ion channel
C FF has been developed. [1] C FF consists of two rigid parts including perfluorinated aromatic units
and linkers connecting them. According to experimental results, C FF transports cations across themembrane and has potassium selectivity.
To assess channel structures of C FF in the membrane-water system at atomic resolution, we
performed quantum mechanical/molecular mechanical (QM/MM) simulation implemented in
GENESIS. [2-5] Specific interactions between the potassium ion in the pore center and theperfluorinated aromatic units were observed: The potassium ion was surrounded by four fluorinesubstituted aromatic units and the ion interacted with eight fluorine atoms, rather than the planes of
the aromatic rings. In addition, different interaction modes were observed in the potassium andsodium ions at the channel entrance: Potassium ion was not completely hydrated and maintainedinteractions with the fluorine atoms. In contrast, sodium ion completely dissociated from C FF and
was hydrated with water molecules, except for the pore center. Comparing these different interaction
modes, the mechanism of the ion selectivity could be understood by a balance of interaction
strengths between the ion and fluorine atoms and between the ion and water molecules.
[1] Sato, K.; et al., Supramolecular Mechanosensitive Potassium Channel Formed by FluorinatedAmphiphilic Cyclophane, J. Am. Chem. Soc., 2022, 144(26), 11802 ‒ 11809; Correction: J. Am.
Chem. Soc., 2022, 144(30), 13983-13984.
[2] Yagi, K.; et al., Anharmonic Vibrational Analysis of Biomolecules and Solvated Molecules
Using Hybrid QM/MM Computations. J. Chem. Theory Comput., 2019, 15, 1924 ‒ 1938.
[3] Yagi, K.; et al., Exploring the Minimum-Energy Pathways and Free-Energy Profiles ofEnzymatic Reactions with QM/MM Calculations. J. Phys. Chem. B, 2021, 125, 4701 ‒ 4713.
[4] Jung, J.; et al., GENESIS: a Hybrid-Parallel and Multi-Scale Molecular Dynamics Simulator
with Enhanced Sampling Algorithms for Biomolecular and Cellular Simulations. WIREs Comput.
Mol. Sci., 2015, 5, 310 ‒ 323.
[5] Kobayashi, C.; et al., GENESIS 1.1: A Hybrid-Parallel Molecular Dynamics Simulator with
Enhanced Sampling Algorithms on Multiple Computational Platforms. J. Comput. Chem., 2017,38, 2193 ‒ 2206.

P01-06

Pursuit of elementary reactions by the difference of H/Dusing the Nuclear-Electronic Orbital method
Kohei Motoki 1
a17.3rsh@g.chuo-u.ac.jp
1

Hirotoshi Mori 1
qc-forest.19d@g.chuo-u.ac.jp

Department of Applied Chemistry, Faculty of Science and Engineering, Chuo University,
1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan

Keywords: Nuclear-Electronic orbital, Deuterium kinetic isotope effect, Elementary reaction
Recent progress in molecular science has shown that the molecular properties of deuteriumcontaining systems would be slightly different from the normal systems. The fact means that a way
to control molecular electronic state without changing its structure has been opened by introducing
Deuterium (D-) instead of substituent groups, often applied in conventional drug discovery. [1,2]A quantum chemical method considering the nuclear quantum effects, i.e., beyond BornOppenheimer (BO) treatment, is mandatory to describe the electronic difference between lighthydrogen and deuterium. Nuclear-Electronic Orbital (NEO) method [3] is one of the beyond BO
methods. Recently, we have initiated a set of deuterium science research related to pharmacy [4].
The most important in the research field is the Kinetic Isotope Effect (KIE). Previously, theKIE has been explained only by the mass effect in quantum mechanics. However, many experimental
results cannot be explained without hypothesizing electronic states' change caused by Dsubstitution. In this study, we have theoretically examined several organic elementary reactions ofwhich KIE have been precisely observed [5]. In the poster session, we will discuss the importanceof electronic state tuning by D- substitution, including a well-known pharmaceutical example case,i.e., an optical isomerization of deuterium-substituted thalidomide [6].
[1] C. Schmidt, First deuterated drug approved, Nature Biochemistry, 2017, 35, 493-494[2] T. Pirali, et. al., Applications of Deuterium in Medicinal Chemistry, J. Med. Chem., 2019, 62,
5276−5297[3] F. Pavosěvic, et. al., Multicomponent Quantum Chemistry: Integrating Electronic and Nuclear
Quantum Effects via the Nuclear−Electronic Orbital Method, Chem. Rev., 2020, 120, 4222−4253[4] K. Motoki, H. Mori, Nuclear-Electronic Orbital study of quantum property changes of organic
molecules by H/D difference, The 16th Molecular Science Symposium Abstract, 2022, 2P098[5] S. Gronert, et. al., Deuterium Isotope Effects in Gas-Phase Reactions of Alkyl Halides:
Distinguishing E2 and S N 2 Pathways, J. Am. Chem. Soc., 1991, 113, 4009-4010[6] C. Tian. et. al., Chem. Eur. J., Enantiomerization Mechanism of Thalidomide and the Role of
Water and Hydroxide Ions, 2012, 18, 14305 – 14313

P01-07

Prediction of Structural Change of siRNA by 2formamide, a Newly-synthesized Chemical Modification,
via Density Functional Theory
Seongjin An 1

Yoshiaki Kobayashi 27826759202@edu.k.u-tokyo.ac.jp

yoshiaki-kobayashi@g.ecc.u-tokyo.ac.jp

Kohei Nomura 3

Yasuaki Kimura 3

nomura.kohei@f.mbox.nagoya-u.ac.jp

kimura.yasuaki.r9@f.mail.nagoya-u.ac.jp

Hiroshi Abe 3

Dai Akase 4h-abe@chem.nagoya-u.ac.jp

dai-akase@hiroshima-u.ac.jp

Misako Aida 4

Kumiko Ui-Tei 1,2

maida@hiroshima-u.ac.jp

ktei@bs.s.u-tokyo.ac.jp

1
2
3
4

The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-Shi, Chiba, 227-8561, JapanThe University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
Hiroshima University, 1-3-2 Kagamiyama, Higashi-Hiroshima City, Hiroshima, 739-8511,
Japan

Keywords: siRNA, 2-formamide, Density Functional Theory, chemical modification, off-target
effectRNA interference (RNAi) is an innate mechanism of post -transcriptional gene silencing mediated bysmall interfering RNA (siRNA) that regulates mRNA expression with complete se quence
complementarity. On the other hand, siRNA often exhibits off-target effect that down-regulates
unintended mRNAs with partial sequence complementarities with the seed region (nucleotides 2 to 8
from the 5´ end) of the guide strand. Our previous report revealed that the degree of off-target effect ispositively correlated with the thermodynamic stability in base -pairing between the seed region of guidestrand and unintended mRNAs [1].
In this study, we introduced newly synthesized 2-formamide chemical modification into sugars of
the guide strand nucleotides, capable of reducing base-pairing stabilities, into seed nucleotides andinvestigated their effects on off-target effect. 2-formamide modification reduced off-target effects at
all of the positions in the seed region. However, the degree s of the off-target effects differed dependingon their positions. The 2-formamides introduced into the former positions (nucleotides 2, 3, 4, 5)
inhibited off-target effects stronger than those in the latter positions (nucleotides 6, 7, 8). Such position
dependency may due to the steric hindrance induced by the sugar modification [2]. To investigate this,we used density functional theory calculation and predicted the structural changes. As the result,structural changes were observed only in the former positions, suggesting that this structural distortion
may have had a great effect on the reduction of off-target effect, and the 2-formamides in the latter
positions are considered to inhibit off-target effects by reducing thermodynamic stabilities.

[1] Ui-Tei, K.; Naito, Y.; Nishi, K.; Juni, A.; Saigo, K. Thermodynamic Stability and Watson –Crick
Base Pairing in the Seed Duplex Are Major Determinants of the Efficiency of the SiRNA-Based
Off-Target Effect. Nucleic Acids Research 2008, 36 (22), 7100–7109.
[2] Iribe, H.; Miyamoto, K.; Takahashi, T.; Kobayashi, Y.; Leo, J.; Aida, M.; Ui -Tei, K. Chemical
Modification of the SiRNA Seed Region Suppresses Off-Target Effects by Steric Hindrance toBase-Pairing with Targets. ACS Omega 2017, 2 (5), 2055–2064.

P01-08

Metadynamics simulation of ferritin encapsulation drugs
Kazutoshi Takahashi 1

kazutoshi.takahashi.pu7@asv.ajinomoto.com

1

Ippei Inoue 1

Research Institute for Bioscience Products & Fine Chemicals, Ajinomoto Co., Inc. 1-1 Suzukicho, Kawasaki-ku, Kawasaki 210-8681, Japan

Keywords: Metadynamics simulation, Ferritin, Protein fluctuation
Metadynamics is one of a computational simulation method that can be used for molecular
dynamics simulation to accelerate rare events. Molecular dynamics simulations are powerful
methods, but it is difficult to perform enough calculations to obtain all the conformations in a typicalmolecular dynamics simulation. If the relevant conformations are separated by a high free energy
barrier, this free energy is rarely exceeded. Therefore, in metadynamics, collective variables (CVs)are set up and a bias potential is added to the CVs to accelerate state transitions in the system andfacilitate sampling. In this study, metadynamics was used to simulate the drug internalization
process of ferritin protein.
Protein-based nanoparticles are one of the carriers in promising drug delivery systems because of
their excellent biocompatibility, solubility, etc. In fact, the iron storage protein human ferritin is an
excellent candidate for a carrier. This protein is a robust cage-like complex composed of 24 identicalsubunits with an external size of 12 nm. Although the conventional method of encapsulation of drug
molecules into ferritin-protein complexes has used the degradation and resealing method, by
incubating ferritin and drug under appropriate conditions, the drug molecules are allowed to passthrough the pores in ferritin to obtain a complex between ferritin and the target drug. The processof passing through the pore was analyzed by metadynamics simulation.
[1] Laio, A.; Parrinello, M., Escaping free-energy minima. Proc Natl Acad Sci U S A 2002, 99 (20),12562-6.
[2] Inoue, I.; Chiba, M.; Ito, K.; Okamatsu, Y.; Suga, Y.; Kitahara, Y.; Nakahara, Y.;
Endo, Y.;
Takahashi, K.;
Tagami, U.; Okamoto, N., One-step construction of ferritin
encapsulation drugs for cancer chemotherapy. Nanoscale 2021, 13 (3), 1875-1883.

P01-09

Simulation study of the interaction between lipids and thecomplex structure of -secretase and APP or Notch.
Chika_Minami 1
minami.373@miyashita-lab.net

Lisa Matsukura 1
liz@miyashita-lab.net

Naoyuki Miyashita 1*
miya@waka.kindai.ac.jp

1

Graduate School of Biology-Oriented Science and Technology, KINDAI University, 930
Nishimitani, Kinokawa, Wakayama 649-6493, Japan

Keywords: Coarse-grained simulation, Molecular dynamics simulation, APP, Notch
-secretase has been known for cleaving various membrane proteins. It consists of four membraneproteins, namely Nicastrine (NCT), Presenilin (PS), APH-1, and PEN-2. Amyloid precursor protein(APP), which is also membrane protein, is cleaved by -secretase and -secretase in the early stageof Alzheimer's disease proceeds. The relation between APP and secretases is linked to the productionof senile plaques. The -cleavage site in the APP has two possible sites, A40 and A42. However,
the mechanism of these selections has not been unknown yet. Notch, the signal transduction
mediating membrane protein, is also known as another one of the targets of the cleavage of secretase. It does not know the mechanism of the binding between -secretase and Notch yet. The
APP and the Notch usually bind to -secretase in the raft environment.
We investigate how lipids affect the docking process between -secretase and APP or Notch. We
performed seven Coarse-grained molecular dynamics simulations, namely APP, Notch, -secretase,-secretase with APP or Notch, -secretase and APP or Notch complex with the membrane. The
membrane contains three lipids, glycerophospholipids, sphingolipids, and cho lesterols. The ratio ofthem is 5:3:2. The distributions of lipids will be shown, and we will discuss the interaction between
the lipids and those membrane proteins.

Fig 1. Coarse-grained model structures of a) -secretase with the membrane, b) secretase and APP with the membrane and c) -secretase and APP complex structurewith the membrane. The green, blue, and red indicate the glycerophospholipids,
sphingolipids, and cholesterol, and the pink, cyan, gray, orange, and yellow show
Nicastrine(NCT), Presenilin(PS), APH-1, PEN-2, and APP, respectively.

[1] Zhou, R et.al, Science 363 (2019)[2]Yang, G et. al, Nature, 565, 192–197 (2019)

P01-10
Conformational behavior and dynamics
of G7A mutant IgG-aptamer
Seiichiro Ishii 1 ,Ryoji Yamazaki 1 ,Ozora Kudo 1 ,Takeshi Ishikawa 2 ,Taiichi Sakamoto 3 ,Kenji Yamagishi 1
yamagishi.kenji@nihon-u.ac.jp

1

College of Engineering, Nihon University, 1 Nakagawara, Tokusada, Tamura, Koriyama,
Fukushima 963-8642, Japan
2
Faculty of Engineering, Kagoshima University, 1 -21-24 Koorimoto, Kagoshima,
Kagoshima, 890-8580, Japan
3
Faculty of Advanced Engineering, Chiba Institute of Technology, 2 -17-1 Tsudanuma, Narashino,
Chiba 275-0016, Japan

Keywords: RNA aptamer, Nucleic Acid, Dynamics, Interaction energy, MD, FMO
RNA aptamers are short, single-stranded oligonucleotides that bind to specific target molecules
such as proteins, nucleic acids, and small molecules. The single strand of RNA aptamers can foldunique tertiary structures that enable the RNA aptamer to interact with high affinity and specificity
for its target molecule. An optimized 24-nucleotide aptamer was designed, which was shown tobind with high affinity to the Fc domain of human Immunoglobulin G (IgG) [1]. RNA mutagenesis
experiments were performed, to investigate the structural and functional contribution of eachnucleotide [1]. Destruction of the interaction between fU6 and G7 by mutation, such a s
fU6-to-U/mU/u and G7-to-A/fU, caused a loss of affinity. Although these data suggest thefunctional importance of the interaction between G7 and U6, the structural basis of the binding
mechanism of the RNA aptamer with IgG is poorly understood.
In this study, in order to elucidate the conformational behavior and dynamics of G7A mutant
IgG-aptamer, we performed the molecular dynamics simulation and fragment molecular orbital
calculation.
Reference[1] Y. Nomura, et al., Nucleic Acids Research, 38, 7822 (2010).
[2] H. Yoshida, T. Ishikawa, T. Sakamoto, K. Yamagishi, et al., Chem. Phys. Letts., 738, 136854 (2020).
(a)

(d)

(c)

(b)

(e)

(f)

Figure. Structure of (a) original IgG aptamer (G7), (b) mutant IgG aptamer (G7A). Distribution of (c) kai angle,(d) G7/A7-Gly402 distance, (e) G7/A7-Arg344 distance, (f) G7/A7-Tyr373 distance, obtained from MD simulation.

P02-01

Energy decomposition analysis for
cyclodextrins-furosemide complexes

1

2

Masao Fujisawa 1

Tomonori Ohata 2

fujisawa@waka.kindai.ac.jp

ohata@fukuoka-u.ac.jp

Hiroyuki Tsutsumi 2

Hirohito Ikeda 2

h-tsutsumi@fukuoka-u.ac.jp

ikeda@fukuoka-u.ac.jp

Department of Biotechnological Science, Kindai University, 930 Nishimit ani, Kinokawa-City,
Wakayama, 649-6493, Japan
Faculty of Pharmaceutical Science, Fukuoka University, 8 -19-1 Nanakuma Fukuoka 814-0180,
Japan

Keywords: Energy decomposition, DFT, Molecular dynamics, Inclusion complex

Cyclodextrin (CyD) can accommodate various kinds of guest molecules in its internal cavity,
recognizing the difference in structure of guest molecules in aqueous solution. Improvement instability and solubility of medicines can be expected when using CyD to form pharmaceu tical drug
conjugates. We determined systematically thermodynamic functions for the molecular inclusion of
simple molecules into - and -CyD cavities in dilute aqueous solutions using microcalorimetry [1-2].
In this study, we have used computer simulation methods e.g., energy decomposition analysis (EDA)
and molecular dynamics (MD) to investigate the thermodynamic aspects of the inc lusion ofpharmaceutical drugs into -CyD cavities, specifically detailing the interaction energy for thecomplexation of furosemide which is a loop diuretic with hydroxypropyl--CyD (Hpc--CyD) inaqueous solutions. Furosemide contains a primary aryl sulfonamide and a furan moiety. We examined
two conformations for each CyD complex, including (1a) the sulfonamide of furosemide existing in the
Hpc--CyD cavity and the furan moiety located on the outside of the secondary hydroxyl group(SULFO_Hpc--CyD), and (1b) the furan ring of furosemide existing in the Hpc --CyD cavity and thesulfonamide located on the outside of the secondary hydroxyl group (FURAN_Hpc --CyD ). Molecular
dynamics calculations were performed for these two conformati ons. The final structure after 20 ns of
MD simulation was analyzed using an energy decomposition method by decomposition into
electrostatic (ES), exchange-repulsion (EX), charge transfer (CT), and dispersion (DI) components.
In the SULFO-Hpc--CyD system at b3lyp/6-31g level, it was realized that dispersion forces ofthis complex by -19.6 kJ mol -1 . Furosemide (SULFO) is stabilized largely on inclusion into
Hpc--CD cavity. The FURAN_Hpc - -CyD system and -CyD systems are will be discussed on
the day of the annual meeting.

[1] Kimura T., Yukiyama T., Fujie S., Fujisawa M., Aki H., Kamiyama T., Enthalpy and entropychanges on molecular inclusion of pentane derivertives into α-cyclodextrin cavities in aqueous
solutions,, Journal of Inclusion Phenomena and Macrocyclic Chemistry, 70(3-4):269-278, 2011.
[2] Ikeda H., Fukushige Y., Matsubara T., Inenaga M., M. Kawahara M., Yukawa M., Fujisawa M.,
Yukawa E., Aki H., Improving water solubility of nateglinide by complexation of β -cyclodextrin,
Journal of Thermal Analysis and Calorimetry, 123(3):1847-1850, 2016.

P02-02

Insight into Allosteric ERK2 Inhibitors by using
Metadynamics Simulations
Hajime Sugiyama 1
sugiyama.hajime.ma@m-chemical.co.jp

Mayu Yoshida 2
yoshida19@b.s.osakafu-u.ac.jp

12

Seisuke Hasegawa 2
se22077x@st.omu.ac.jp

Takayoshi Kinoshita 2
kinotk@omu.ac.jp

Mitsubishi Chemical Corporation, 1000 Kamoshida-cho, Aoba-ku, Yokohama, Kanagawa
227-8502, Japan
Osaka Metropolitan Univ., 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan

Keywords: ERK2, Allosteric site, Metadynamics
ERK2 (Extracellular signal-regulated kinase 2) is a member of the mitogen-activated protein
kinase family. STAT3 (signal transducer and activator of transcription 3), which plays an es sential
role in normal glucose homeostasis which plays an essential role in normal glucose homeostasis,and regulates cell proliferation, differentiation, and various other cellular responses. Based on
previous studies on the ERK2/STAT3 pathway, the author s focused on ERK2 as a candidate target
molecule for diabetes treatment and identified small molecule compounds with inhibitory activity
by in silico screening [1].
In order to establish the basis for drug design, we investigated the static interaction be tween
ERK2 and its compounds in the bound state from the crystal structure, aiming at structuralmodification of the small molecule compounds. On the other hand, the association and dissociation
processes are also important in drug design. In or der to efficiently generate these events in a timescale that allows molecular dynamics simulations, we attempted to apply a combined technique ofMetadynamics [2] and Hamiltonian Replica Exchange [3].
As a result, we confirmed the occurrence of these events during the calculation and succeeded in
calculating the free energy surfaces. The detailed results of the analysis will be presented on thesame day. These results would greatly enhance to develop highly potent and selective ERK 2
inhibitors.
[1] T. Kinoshita, et al., Chem. Biol. Drug Des. 78: 471- 476 (2011).
[2] A. Barducci, et al., Wiley Interdiscip. Rev.: Comput. Mol. Sci. 1: 826−843 (2011).
[3] V. Oleinikovas et al., J. Am. Chem. Soc. 138: 14257−14263 (2016).

P02-03

Interaction analyses of inhibitors against threonyl-tRNA
synthetase by fragment molecular orbital calculations
Yoshiharu Mori
ymori@landscape.kobe-u.ac.jp

Shigenori Tanaka
tanaka2@kobe-u.ac.jp

Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada, Kobe, Hyogo
657-8501, Japan

Keywords: Electronic structure calculation, Protein-ligand interaction, Translation inhibitor
Threonyl-tRNA synthetase (ThrRS) is an enzyme that covalently binds threonine to tRNA and is
one of a group of enzymes required for translation processes. They are also required for protein
synthesis and are therefore the target of inhibitors. Borrelidin is a well-known inhibitor, and it isunderstood from a structural analysis that this compound occupies several parts of the ligand
binding region necessary for enzyme function. However, because borrelidin can also bind to humanthreonyl-tRNA synthetase, alternative compounds that bind to bacterial ThrRS but not to human
ThrRS are desirable. Recently, such inhibitors have been discovered and synthesized. In this study,
we used the fragment molecular orbital (FMO) method to analyze the interactions between suchcompounds and ThrRS to find similarities and differences in the binding mode between species.
The structures of human ThrRS bound with borrelidin and E. coli ThrRS bound with borrelidin
were selected for the interaction analyses. Structural optimization using the Amber force field was
performed on these structures to obtain structures for the FMO calculations. The FMO calculations
were performed at the MP2/6-31G* level of theory. Interaction analyses were performed using thepair interaction energy decomposition analysis (PIEDA).
Because borrelidin forms a salt bridge with ThrRS, the contribution of electrostatic interactions
was the largest in both species. On the other hand, in human and E. coli, there were variations in
the interactions due to differences in the amino acid sequence of the ligand binding site. For
comparison, FMO calculations were also performed on a structure containing an alternative
compound with low binding affinity to human ThrRS. The interaction analyses provide insights into
the inhibition mechanism for ThrRS.

P02-04

Amino acid preference mapping on protein-protein
interaction surface using mixed-solvent molecular
dynamics
Genki Kudo 1

Keisuke Yanagisawa 2s2120165@s.tsukuba.ac.jp

yanagisawa@c.titech.ac.jp

Ryunosuke Yoshino 3,4

Takatsugu Hirokawa 3,4

yoshino.r.aa@md.tsukuba.ac.jp

t-hirokawa@md.tsukuba.ac.jp1

Physics Department, Graduate School of Pure and Applied Sciences, University of Tsuk uba,
1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan2
Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2 -12-1
Ookayama, Meguro 152-8550, Tokyo, Japan
3
Faculty of Medicine, University of Tsukuba, 1 -1-1 Tennodai, Tsukuba 305-8575, Ibaraki, Japan
4
Transborder Medical Research Center, Univer sity of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki,305-8577, Japan

Keywords: Protein-Protein Interaction, Mixed-Solvent Molecular Dynamics, Hotspot Detection
Over the past decade, compounds with a molecular weight range of 700 -2000, such as cyclic
peptides, have been developed as protein-protein interaction (PPI) inhibitors [1]. Several reports
identified hotspots on PPI surface using mixed-solvent molecular dynamics (MSMD) which
involves MD in the presence of explicit water molecules mixed with probe mol ecules or functionalgroup fragments [2]. However, accessibility and amino acid preference on PPI surface has not been
evaluated because they employed probes which are substructures of traditional drug molecules.
In this study, we propose MSMD with amino acid probes to detect hotspots on PPI surfaces. In
addition, amino acid probes can also provide their preferred surfaces (Figure 1). In ourpresentation, we assessed our method with several proteins, and compared amino acid preference
obtained from MSMD and PPI interface residues obtained from co-crystal structures.
(a) Arg probes(b) Glu probes

Figure 1. Example of preference maps on PPI surface obtained from MSMD
[1] Dougherty, P.G.; Qian, Z.; Pei, D. Macrocycles as protein-protein interaction inhibitors,
Biochem J., 2017, 474(7), 1109-1125.
[2] Ghanakota, P.; et al., Large-scale validation of mixed-solvent simulations to assess hotspots at
protein–protein interaction interfaces, J Chem Inf Model., 2018, 58(4), 784-793.

P02-05
Practical visualization of interaction energies by FMO
for drug design
Hirofumi Watanabe 1

Yuji Tanakamaru 1

watanabe@withmetis.jp

1

WithMetis Co.,Ltd., Wembley Building 7F 6-1-17 Isogamidori, Chuo-ku, Kobe, Hyogo, 6710086, Japan

Keywords: Fragment molecular orbital method (FMO), Inter-fragment interaction energy (IFIE),
Computer-aided drug design (CADD), Protein–ligand interaction
Inter-fragment energy (IFIE), evaluated by the fragment molecular orbital (FMO) method, is useful
information for analyzing protein–ligand interactions based on quantum mechanics [1-4]. Because
IFIEs between amino acid residue units or even smaller subunits can be further decomposed into
components based on their physical origin (PIEDA) [5], the FMO method is a powerful tool forcompound design in lead generation and lead optimization processes. To make this method more
useful and more intuitive, we are developing a visualization tool for the IFIEs, Q -AIR FLAIR
Viewer. The non-covalent molecular interactions are classified by composed elements andgeometries, such as hydrogen bonds, salt bridges, and weaker interactions, such as CH–, –interactions, where the contribution of dispersion interactions is important. This tool supports
interpretation of the relationship between IFIEs and these molecular interactions based on structural
information. We will discuss how we should visualize the IFIEs and PIEDAs in the research in thepharmaceutical company. Example analysis was done using data in a previous p38 study [6] and in
the FMO DD [7].
[1] Kitaura, K.; et al., Fragment molecular orbital method: an approximate computational method
for large molecules, Chemical Physics Letters, 1999, 313, 701-706.
[2] Tanaka, S.; et al., Electron-correlated fragment-molecular-orbital calculations for biomolecularand nano systems, Physical Chemistry Chemical Physics, 2014, 16, 10310-103.
[3] Nakano, T.; et al., Fragment molecular orbital method: use of approximate electrostatic potential ,Chemical Physics Letters, 2002, 351, 475 – 480.
[4] Mochizuki, Y.; et al., Large scale MP2 calculations with fragment molecular orbital scheme ,Chemical Physics Letters, 2004, 396, 473-479.
[5] Fedorov, D.; et al., Pair interaction energy decomposition analysis Journal Computational
Chemistry, 2007, 28, 222–237.
[6] Sheng, Y.; Watanabe, H.; et al., Towards good correlation between fragment molecular orbital
interaction energies and experimental IC50 for ligand binding: A case study of p38 MAP kinase ,Computational and Structural Biotechnology Journal, 2018, 16, 421-434.
[7] Takaya, D.; et al., FMODB: The World's First Database of Quantum Mechanical Calculations
for Biomacromolecules Based on the Fragment Molecular Orbital Method , Journal Chemical
Information Modeling, 2021, 61, 777-794.

P03-01

Investigation of the applicability domain for prediction
model using in-house data
Toshiaki Watanabe 1

Kosuke Takeuchi 1

watanabe.toshiaki.h5@daiichisankyo.co.jp

Ryo Kunimoto 1

1
2

Atsushi Kasuya 1

Kentaro Rikimaru 2

Daisuke Sugiyama 1

Shin Saitou 2

R&D Division, Daiichi Sankyo Co., Ltd., 1-2-58 Hiromachi, Shinagawa-ku, Tokyo 140-8710,
Japan
ExaWizards Inc., Tokyo 105-0021, Japan

Keywords: machine learning, applicability domain
The drug discovery process would be expected to accelerate by using machine learning methods.
On the other hand, if not used appropriately case, there is a risk of misleading rather than improving
efficiency. We thought that the understanding of AD (Applicability Domain) 1,2,3 is an essential
element to properly interpreting the predicted results. Therefore, we conducted an analysis
concerning AD using in-house data.
Investigation of AD using in-house metabolic stability data, we found that the distance between
training and test affected the accuracy. By designing a validation set based on this, we succeeded in
improving the accuracy. In a case study of in-house project data, it became possible to determine
the applicability of the global model using the AD concept. Furthermore, we found that switching
to a local model is effective once data has been accumulated as the project progresses.

[1] Baskin, I. Kireeva, N. and Varnek, A. The One-Class Classification Approach to Data
Description and to Models Applicability Domain. Mol. Inf. 2010, 29, 581-587.
[2] Sushko, I. et al. Applicability Domains for Classification Problems: Benchmarking of Distanceto Models for Ames Mutagenicity Set. J. Chem. Inf. Model. 2010, 50, 12, 2094–2111.
[3] Kaneko, H. and Funatsu K. Applicability Domain Based on Ensemble Learning in Classification
and Regression Analyses J. Chem. Inf. Model. 2014, 54, 9, 2469–2482.

P03-02

Deep Learning for Cellular Morphological Change
Detection with High Throughput Images
Yuko Nirei 1
nirei.yuuko@ma.mt-pharma.co.jp

1

2

Kentaro Matsumoto 2
matsumoto.kentarou@ma.mt-pharma.co.jp

Ken Ohno 2

Ryuta Saito 1

oono.ken@mr.mt-pharma.co.jp

saitou.ryuuta@mc.mt-pharma.co.jp

Discovery Technology Laboratories, Sohyaku. Innovative Research Division, Mitsubishi
Tanabe Pharma Corporation, 1000, Kamoshida, Aoba, Yokohama, Kanagawa, 227 -0033, Japan
Discovery Technology Laboratories, Sohyaku. Innovative Research Division, Mitsubishi
Tanabe Pharma Corporation, 2-26-1, Muraoka-Higashi, Fujisawa, Kanagawa, 251-8555, Japan

Keywords: image processing, high content screening, CellProfiler, deep learning, transfer learning
Cellular morphological changes observed in microscopic images of high -content screening (HCS)
are detected by various image processing tools including software solutions designed for HCS.
Software for HCS offers feature sets such as area, shape, and texture of cellular structures multi stained with fluorescence and measured in different color channels [1]. These features are designed
for detecting various morphological changes observed in cells, which are comprehensible, however,can lead to limitation in detecting those unexpected in the design. In contrast, deep learningalgorithms can represent incomprehensible, but complex and unnoticeable features observed inmorphological changes. Especially, features of deep neural networks pre-trained with various image
objects are applicable to different tasks and can achieve high accuracy and speedy processing. Here,we clarify advantages of applying deep learning to HCS, and compare between an image processing
software for HCS, CellProfiler (version 1) [2], and one of the deep neural networks, AlexNet, in
hit-judgement of morphological changes induced by various chemical compounds and theprocessing speed. We prepared images of U2OS cells processed with 1680 compounds or DMSO.
The cell structures were multi-stained with fluorescent, which were observed in four channels foreach cell. Morphological changes induced by each compound w ere measured with 1085 features in
CellProfiler. Significant morphological changes were identified by five-fold SD from DMSO,
resulting in 228 hit and 1452 non-hit compounds. For the same image dataset, 4097 features of pretrained AlexNet model were extracted from individual cellular images. Based on these AlexNet
features, cells processed with each compound and DMSO were binary classified with k -NN, whereaccuracy of a classification is expected to increase when a compound induced larger morpho logicalchanges from non-treated cells. The average accuracy of classifications with AlexNet based method
for the 228 CellProfiler hits was 67.7 ± 7.8%, and for the 1452 non-hit compounds was 58.2 ± 2.5%.
Among the non-hit compounds, 290 compounds showed more than 60% accuracy with AlexNet
based classifications between each compound and DMSO, indicating that AlexNet has capability todetect morphological changes that are not detected with predefined feature s of CellProfiler. In
addition, processing speed on our local computational environment was about three months with
CellProfiler, and about two and a half days (33 seconds for a compound well) with the AlexNet
based method. These results show that deep learning can offer higher capability in detecting
morphological changes and faster processing compared to software solutions designed for HCS.
[1] Lin, S.; Schorpp, K; Rothenaigner, I; Hadian, K. Image-based high content screening in drug
discovery, Drug Discovery Today, 2020, 25, 1348-1361.
[2] https://cellprofiler.org/

P03-03
Investigation of the relationship between the performanceof Encoder-Decoder model and the characteristic ofmolecular representation obtained
Shumpei Nemoto 1
nemo88@g.ecc.u-tokyo.ac.jp

Tadahaya Mizuno 1, †Hiroyuki Kusuhara 1, †tadahaya@mol.f.u-tokyo.ac.jp

kusuhara@mol.f.u-tokyo.ac.jp

1

Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The
University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan†
Author to whom correspondence should be addressed

Keywords: Encoder-Decoder model, chemoinformatics, molecular representation, descriptor
In chemoinformatics tasks such as virtual screening and toxicity prediction, it is essential todescribe chemicals as numerical information (molecular representation) beforehand. Since theaccuracy of downstream chemoinformatics tasks depends on the nature of the molecular
representation various methods have been developed. After the first proposal by Gomez Bómbarelli et al. in 2016, methods using Encoder-Decoder (ED) models, inspired by natural
language processing and takes SMILES as input, have attached much attention for generating
molecular representation[1]. Molecular representation derived from the ED model -based methods
has two characteristics: continuous and structure -returnable natures. Many methods have beendeveloped, however, most of them evaluate the performance of their methods based on theaccuracy of downstream tasks using molecular representations as inputs. This approach canevaluate the ability of molecular representations to describe chemical structures, but it cannot
evaluate the ability to return to structure. In addition, most of the approaches are comparisons
between models, and the relationship between model accuracy itself and the performance of themolecular representation is unclear.
The purpose of this study is to clarify the relationship between the performance of ED model as
an NLP model (translation accuracy) and a generator of molecular representation. In particular,
since the structure representation ability of the molecular representation is related to the encoder
performance and the structure restoration ability is related to the decoder performance of ED
model, we tried to evaluate them separately.
Using ZINC dataset as input, we prepared a set of ED models with various translation accuracies
by controlling the progress of training. To evaluate the structure representation ability, predictivemodels for ToxCast HTS assay data were built using molecular representation derived from the ED
model set as input. Interestingly, accuracy for these downstream tasks was similar, independent of
accuracy across model sets, except for the model with the poorest translation accuracy, suggestingthat the structure representation ability of molecular representation is acquired early in the training.
To evaluate the structure restoration ability, we firstly collected chemicals successfully translated(output structure is the same as input) in all accuracy models. Random numbers with specificcorrelation with molecular representations of these chemicals were generated for each model andthe characteristics of the structures restored from the random numbers were evaluated with somemetrics of chemical sets such as diversity. The results showed that the higher the accuracy of the
ED model, the more it converged to the original structure , suggesting that enough training isnecessary for unique structure restoration.
This study suggests that, in structure numerization using ED model, a high-performance modelis unnecessary when the objective is only to capture structural features, while a high -performancemodel is necessary when the objective includes structure restoration.
[1] Rafael Gomez- Bómbarelli ; et al, Automatic Chemical Design Using a Data-Driven Continuous
Repreesntation of Molecules, ACS Central Science, 2018, 4, 268-276

P03-04

Integrated browsing of chemical reactions registered inElectronic Lab Notebook and external data

1

Yu Endo 1

Akihiro Oda 1

yu.endo@po.nippon-shinyaku.co.jp

a.oda@po.nippon-shinyaku.co.jp

Nippon Shinyaku Co., Ltd., 14, Nishinosho -monguchi-cho, Kisshoin, Minami-ku, Kyoto,
Kyoto, 601-8550, Japan

Keywords: Electronic Lab Notebook, ELN, data integration , Reaxys
Nippon Shinyaku has integrated intrinsic reaction information within the Elsevier Reaxys product.
The incorporation and discoverability of proprietary and public information significantly improve s
researcher productivity. With this system, researchers can search Reaxys for internal Electronic Lab
Notebook (ELN) data and external data published in journals and patents at the same time, unifying
and organizing search results that are directly relevant to the researcher's workflow.
To achieve this integrated workflow, we developed a BIOVIA Pipeline Pilot protocol to automate
a series of steps: extraction of reaction data from ELN, conversion to UDM 1 data format, anduploading of the converted UDM files to Amazon S3 buckets.
Key points of the ELN integration and data modeling will be discussed.
[1] Pistoia Alliance UDM Project page https://www.pistoiaalliance.org/projects/current projects/unified-data-model/ (accessed 2022-08-08)

P03-05

Construction D4 environment for Patent informatics
Takayuki Serizawa 1serizawa.takayuki.a3@daiichisankyo.co.jp
1

Ryo Kunimoto 1

kunimoto.ryo.si@daiichisankyo.co.jp

R&D Division, Daiichi Sankyo Co., Ltd., 1-2-58 Hiromachi, Shinagawa-ku, Tokyo 140-8710, Japan

Keywords: Database Development, Open Data Collection
A patent is one of the useful data sources in drug discovery projects. It has not only structural data
but also biological data.
Chemoinformatician often spends lots of time to extract data from patents because the information
is not provided as machine readable format, like PDF from OCR.
To overcome the issue, we build in-house patent database with public data source, SureChEMBL [1] .
Here we would like to present our internal effort to construct patent analysis infrastructure on AWS
cloud.
(D4: Data Driven Drug Discovery)[1] George Papadatos et al., Nucleic Acids Research, Volume 44, Issue D1, 4 January 2016, Pages
D1220–D1228[2] https://github.com/chembl/surechembl-data-clientP03-06

Estimation of amino-acid interaction potentials
with structure similarity calculated by Digital Annealer
Chieko Terashima 1

Yusuke Yoshida 1

c.terashima@jp.fujitsu.com

Tetsuya Onuki 1
onuki.tetsuya@fujitsu.com

1

yoshida_yusuke@fujitsu.com

Hiroyuki Satou

shryk@fujitsu.com

Go Fujisawa

fujisawa.go@fujitsu.com

Fujitsu Limited. 1-5 Omiya-cho, Saiwai-ku, Kawasaki-shi, Kanagawa, 212-0014, Japan

Keywords: amino-acid, interaction potential, machine learning, structure similarity
Cyclic peptide is a kind of the promising molecules for middle drug discovery, and their structure
is important. For structure investigation, a coarse-grained model with amino-acid pair interactions
was treated [1]. Amino-acid pair interactions developed by umbrella sampling [2], require a longtime and enormous computational resources. To use the coarse-grained model, amino-acid pairinteractions were estimated by machine learning with structure similarity calculated by Fujitsu
Quantum-inspired Computing Digital Annealer [3, 4] (Figure 1). The estimated model from thisdescriptor shows higher accuracy than other structural descriptors.
For searching the estimation model, we tried two types of data segmentation methods. First, thedata of amino-acid pair interactions were randomly divided. Second, amino-acid was divided into alearning data group or validation data group. As the result, it was easy to estimate the amino-acidpair interaction with the known amino-acid, but it was difficult to estimate the interaction with the
unknown amino-acid. On the day, we’ll show the solution way.

Figure1: Structure similarity calculated by Digital Annealer[1] Droi T., Ron E PROTEINS: Structure, Function, and Genetics 41:40–46 (2000).
[2] GROMACS Tutorials http://www.mdtutorials.com/gmx/index.html.
[3] https://www.fujitsu.com/global/services/business-services/digital-annealer/[4] M. Aramon, G. Rosenberg, E. Valiante, T. Miyazawa, H. Tamura, H. G. Katzgraber,
Frontiers in Physics 2019, 7.

P03-07

Recent developments of FMODB: enhancement of the
IFIE/PIEDA interface for analyzing the related FMO
calculation data of bio-macromolecules
Kikuko Kamisaka 1

Chiduru Watanabe 1

kikuko.kamisaka@riken.jp

chiduru.watanabe@riken.jp

Daisuke Takaya 2
takaya-d@phs.osaka-u.ac.jp

Teruki Honma 1
honma.teruki@riken.jp

1

2

RIKEN Center for Biosystems Dynamics Research, 1 -7-22 Suehiro-Cho, Tsurumi-Ku,
Yokohama, Kanagawa 230-0045, Japan
Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka
565-0871, Japan

Keywords: FMO, Database, MD Snapshots, interaction energy diagram
The fragment molecular orbital (FMO) method, one of the quantum chemical calculations, canquantitatively and accurately estimate the inter-fragment interaction energy (IFIE) in ligand-protein,protein-protein, and nucleic acids systems. In addition, the pair interaction energy decomposition
analysis (PIEDA) enables a more detailed interaction analysis. Therefore, FMO is expected to be
helpful in drug discovery research, such as selecting lead compounds and compound design.
Our group has accumulated FMO data calculated by skilled researchers from the FMO drug design(FMODD) consortium and made it public as an FMO database (FMODB [1] ). The database provides
simple analysis functionality such as a graph of IFIE/PIEDA values via a web interface. Recently,
in FMODB, there has been an increase in registration of FMO calculation data for a series of datasuch as snapshot time-series structure of molecular dynamics (MD) simulation and docking pose of
a complex. However, the IFIE/PIEDA analysis functionality provided via the web interface in the
database was only for single calculation data. We have developed new functionality to collectively
analyze ligand-protein interactions for multiple structures registered in FMODB, such as a serie s ofdatasets of MD snapshots. In this study, we introduce the ability to collectively analyze theinteractions between protein-ligand, using MD snapshots [2] for complexes of cyclin-dependent
kinase-2 (CDK2) with six inhibitors (PDB IDs: 4FKL, 4FKI, 4FKQ, 4FKR, 4FKS, and 4FKW). We
also derive the correlation between the dynamically averaged IFIE/PIEDA and the activity value
considering structural fluctuations.
In addition, we developed a function to automatically generate an interaction energy diagram thatconsiders the bond network and its strength between fragments using IFIE/PIEDA. The diagram
will be helpful for analyzing and understanding intricate bound networks and their interactionenergy. Thus, we introduce the function and usage using the complex between Remdesivir, RNA,and RNA-dependent RNA polymerases [3] .
Acknowledgment
The authors thank Dr. Kazumi Tsuda, Mr. Daisuke Murayama, Dr. Shu Koyama, and Dr. Tomoharu
Isobe of Science & Technology Systems, Inc., Japan, for technical support. This research was donein activities of the FMO Drug Design Consortium, https://fmodd.jp/top-en/ .
[1] Takaya, D.; et al., J. Chem. Inf. Model. 2021, 61, 2, 777–794.
[2] Takaba, K.; et al., J. Comput. Chem., 2022, 43(20), 1362-1371.
[3] Kato, K.; et al., J. Mol. Graph. Model., 2020, 100, 107695.

P03-08

Reservoir Computing for Efficient Prediction of Optical
Response of Digital Metamaterial
Tomoya Meguro 1
phf67gbv@s.okayama-u.ac.jp

Kei Tokita 2
tokita@i.nagoya-u.ac.jp
1

Masaaki Misawa 1
misawa@okayama-u.ac.jp

Kenji Tsuruta 1
tsuruta@okayama-u.ac.jp

Graduate School of Natural Science and Technology, Okayama University,
3-1-1 Tsushimanaka, Kita-ku, Okayama-shi, Okayama 700-8530, Japan
2
Graduate School of Informatics, Nagoya University,
Furo-cho, Chikusa-ku, Nagoya-shi, Aichi 464-8601, Japan

Keywords: Machine Learning, Reservoir Computing, Digital Metamaterials
Application of artificial intelligence technology has been rapidly expanding in a variety ofindustries, yet it requires big data to acquire sufficient accuracy in the application. In this study, we
focus on an artificial material called “digital metamaterials.” Metamaterials are artificial materials
that exhibit exotic responses to incident waves, such as light and sound, in ways not found in nature.
By periodically arranging metallic structures smaller than the wavelength of light, they have unique
optical properties depending on their shape. The principle of operation is that the metallic
nanostructure acts as an antenna in which the optical response is triggered by resonance at afrequency corresponding to the length of the structure. As a specific example, it is expected to be
applied to a sensor, which can be used to detect nanostructures simply by exposing them to infrared
light. Conventional approach to the design of metamaterials relies strongly on the human experienceand wisdom along with try-and-error processes. Digital metamaterial [1] is a two-dimensional
nanostructure that is represented by a pixel pattern with the data structure similar to that in thesequences of genes. Application of machine learning technologies to optimize metamaterials
properties with respect to such digitized data structure is expected to lead to a high-throughput
development of next-generation devices.
Here we adopt a machine learning model called reservoir computing, which has shown high utility
for temporal series of data, to predict the optical response of metamaterials. Reservoir computing
is a type of artificial neural network, but unlike other models, it achieves a reduction ofcomputational cost by partially fixing neural network (reservoir) and by optimizing only a readoutlayer. We have previously reported that the precise prediction of the digital metamaterial becomes
more difficult by using either an artificial neural network or a metaheuristic algorithm as the
structure be more complex [2]. Based on this, we here apply reservoir computing to the digital
metamaterial, expecting to improve the prediction accuracy of data with complex structures while
reducing the computational cost. Also, the Fast Fourier Transform (FFT) for the structural data isapplied as a preprocessing method for an effective feature extraction from the input data [3]. In thispresentation, we will report the results of reservoir computing, FFT processing, and theireffectiveness on the accuracy specifically in terms of MAE (Mean Absolute Error).
[1] C. D. Giovampaola and N. Engheta, Digital metamaterials, Nat. Mater. 13, pp.1115-1121 (2014).
[2] Y. Sugino, A. Ishikawa, Y. Hayashi, and K. Tsuruta, Automated design of infrared digital
metamaterials by genetic algorithm, Proc. SPIE 10343, 103432O (2017).
[3] N. Md Isa, A. Amir, M. Ilyas and M. Razalli, Motor imagery classification in Brain computerinterface (BCI) based on EEG signal by using machine learning technique, Bull. Electr. Eng.
Inform. 8, pp. 269-275, 2019.

P03-09
Assessment of iterative screening for drug discovery
using an unbiased dataset
Akira Kimura 1
akira.yamazaki@riken.jp

1

Tomohiro sato 1
tomohiro.sato@riken.jp

Teruki Honma 1
honma.teruki@riken.jp

Center for Biosystems Dynamics Research, RIKEN, 1 -7-22, Suehiro-cho, Tsurumi-ku,
Yokohama, Kanagawa 230-0045, Japan

Keywords: drug screening, high throughput screening, machine learning, AI
In drug discovery, screening of compound libraries for hit identification is time-consuming, laborintensive, and expensive process. Iterative screening is a technology that improves the efficiency
by utilizing machine learning to iteratively select a batch of compounds in order of their expected
activities based on the assay results of previous batches, and there are several reports about its effect.
However, there are concerns that the data sets conventionally used to assess iterative screening may
contain issues about data biases such as false positive compounds or too high ratio of hit compounds,
which could distort the assessment of screening efficiency.
In this study, we examined the effect of iterative screening using a data set named LIT-PCBA, whichis created form PubChem BioAssay and processed to remove biases. Specifically, we performed
iterative screening using several machine learning and deep learning models (DNN, LGBM, RF,
SVM, XGB). For three targets in LIT-PCBA dataset, approximately 70% of the active compounds
were obtained when 35% of the library was screened, which was roughly the same retrieval aspreviously reported. On the day of the conference, we plan to discuss the results including active
learning, hyperparameter tuning, and so on.

Figure1. Recovery ratio of active

Figure2. Difference between normalcompounds during screening

screening and iterative screening

化合物の回収率[1] Macarron R. ; et al., Impact of High-Throughput Screening in Biomedical Research, Nat. Rev. Drug
Discov, 2011, 10, 188-195.
[2] Viet-Khoa Tran-Nguyen ; et al., LIT-PCBA: An Unbiased Data Set for Machine Learning
and Virtual Screening, Journal of Chemical Information and Modeling , 2020, 60(9), 4263-4273.
[3] Gabriel H.S. Dreiman ; et al., Changing the HTS Paradigm: AI-Driven Iterative Screening for Hit
Finding, SLAS Discovery, 2021, 26(2), 257-262.
[4] Isidro Cortés-Ciriano; et al., Discovering Highly Potent Molecules from an Initial Set of Inactives
Using Iterative Screening, Journal of Chemical Information and Modeling , 2018, 58(9), 2000-2014.

P03-10

Constructing a systematic knowledge graph using realworld data
Takeshi Takagi 1

Hideki Inoue 1

Atsushi Kikkawa 1

takagi.takeshi489@eps.co.jp

inoue.hideki296@eps.co.jp

kikkawa.atsushi109@eps.co.jp

Yukio Kitajima 1
kitajima.yukio181@eps.co.jp

1

Nobuyuki Okajima 1
okajima.nobuyuki505@eps.co.jp

EPS Corporation, Tsuruya Building, 2-23 Shimomiyaibicho, Shinjuku-ku, Tokyo, 162-0822,
Japan

Keywords: Real-world data, Knowledge graph, tf-idf
Objective: A knowledge graph is a knowledge base that systematically connects various types of
information and represents them in a graph structure. By representing both structured andunstructured data as a knowledge graph, it becomes easier to analyze the relationship of data.
In this study, we investigated a method for systematically creating knowledge graphs from tabular
data, performed transformations using the investigated method on actual real -world data, andexamined methods for evaluating the validity of the created knowledge graphs.
Methods: Using the medical information database (RWD-DB) operated by Real World Data, Co.,
Ltd. , we created a knowledge graph based on the method of Linfeng et al 1 .
For each patient, only the receipt disease names that matched the electronic medical record diseasenames were extracted, and patient information, diseases, drugs, and laboratory values were obtainedfor each month. Data represented by continuous numerical values, such as laboratory values, werecategorized as above/below the reference value and represen ted as nodes in the graph structure.
Next, tf-idf used in natural language processing was applied as a method to evaluate the validity of
the knowledge graph. The tf-idf used in natural language processing is an index that expresses “howimportant each word is within each document.” In this study, a disease was considered as a document
and a laboratory test value as a word, and the tf-idf value was calculated with the intention todetermine “how important each laboratory test value is for the disease.”
Results: In disease E11 (type 2 diabetes mellitus), laboratory values indicating high HbA1c anddecreased renal function had higher tf-idf values. In N18 (chronic kidney disease), laboratory values
indicating decreased renal function also had higher tf-idf values.
Discussion: The above results suggest that the knowledge graph created in this study captures thecharacteristic laboratory values of each disease. Knowledge graphs using medical data are expected
to be applied to support diagnosis and drug presc riptions in the medical field in the future of
medicine 2 .
[1] Linfeng, L.; Peng, W.; Jun, Y.; et al., Real-world data medical knowledge graph: construction
and applications, Artificial Intelligence In Medicine, 2020, 103, 101817.
[2] Huang, X.; et al., A knowledge graph based question answering method for medical domain.
PeerJ Comput Sci, 2021, 7, e667.

P03-11
Proposal and Outline of
the “Autonomous Chemistry” Research
Kohtaro Yuta
k-yuta@insilicodata.com

In Silico Data, Ltd., 5-19-5, Akitsu, Narasino, Chiba, 275-0025, Japan

Keywords: Autonomous Chemistry, Data Science, Autonomous drug design
The advocacy of "autonomous chemistry" is the first presentation in Japan (1). In terms oftechnology, “autonomous” is the evolution of technology that comes after automation, and is a formof technology in the near future (Figure1). Currently, the word "autonomous chemistry" is extremelyrare in the world. In this presentation, we will explain the outline of “au tonomous chemistry”
research and discuss "autonomous drug design", which is one of the application cases.
Once the definition and basis of "autonomous chemistry" are determined, it will be applicable to all
research fields related to chemistry (drug discov ery, toxicity, physical properties, functional
compounds, food, etc.). The difference between “automation” and “autonomy” lies in the high levelof human judgment. There are no judgment items in “automation”, but “autonomous” ischaracterized by a large number of advanced and complex judgment items.
Autonomous is strongly deployed in the automotive field (Fig ure2). Next-generation automateddriving is “autonomous driving,” and research examples in this field can be used as references forapplications in the field of chemistry. Autonomous driving has 6 levels, from level 0 to 5, until
completion. (2) Technology is accumulated sequentially from the starting level 0 (no automation),and it will be completed at level 5 (complete driving automation).
“Autonomous chemistry” in the field of chemistry also sets a level so that the degree of completioncan be judged by the value of this level. For example, the implementation of drug design requires
processing and judgment of various information, and there are extremely c omplicated processes
such as evaluation of toxicity and pharmacological activity, and reorganization of projects.
It is difficult to solve drug design research involving such complicated decisions by simple“automation”, and “autonomous” research can handl e it. (Figure3).
In real drug design process, complex technologies are continuously applied and sophisticatedjudgments are made according to changing circumstances. This is the category of autonomous drug
discovery. Autonomous drug design is carried out whil e the system judges the collaboration andcontinuity of a wide variety of technologies required for drug design. In other words, Dr. Horii
proposes in his lecture (3) that "an approach to system construction from the point of contact
between artificial intelligence and human intelligence is desired as a future prospect". Autonomous
drug discovery is exactly what makes this possible.
[1] http://www.insilicodata.com/index-others.html[2] https://www.macnica.co.jp/business/maas/columns/135343/[3] https://cbi-society.org/taikai/taikai21/FS/FS-07.pdf

Figure 1. Technological advancements

Figure 2. 5 Autonomous Driving Levels

Figure 3. Level division of the drag design processaccording to the autonomous concept

P03-12

Protein-ligand complex structure generation withdiffusion-based generative models
Shuya Nakata 1
218x219x@stu.kobe-u.ac.jp

Yoshiharu Mori 1
ymori@landscape.kobe-u.ac.jp

Shigenori Tanaka 1
tanaka2@kobe-u.ac.jp

1

Graduate School of System Informatics, Kobe University, Japan

Keywords: Ligand docking, Machine learning, Generative model
The complex structures of proteins and ligand molecules provide valuable informati on
about their interactions and are important for structure-based drug design. Docking
simulations are widely used to computationally predict complex structures, but the
number of degrees of freedom that can be addressed is limited , making it difficult toobtain accurate results using uncomplexed structures.
In this study, we developed a machine learning model to generate protein -ligand
complex structures taking into account both receptor and ligand flexibility, which isdifficult to achieve with conventional methods. To generate complex structures allowing
for conformational changes upon binding, we extended and applied the diffusion -based
generative models [1,2] to the protein-ligand complex structures in the PDBbind
database and directly modeled the 3D coordinates of protein Cα atoms and ligand nonhydrogen atoms. Using a low-resolution distance matrix between protein residues anda 2D structure of the ligand as input, trained models were able to generate a variety ofcomplex structures, including ones close to the experi mental structure (L-RMSD<3Å).
The details of the method and the results of the evaluation of the generat ed structures will be
discussed in the presentation.
[1] Kingma, D., Salimans, T., Poole, B. and Ho, J. Variational Diffusion Models, NeurIPS, 2021,
34, 21696-21707[2] Hoogeboom, E., Satorras, V., Vignac, C. and Welling, M. Equivariant Diffusion for Molec ule
Generation in 3D, arXiv:2203.17003, 2022

P03-13Development of a method for building a long-term diseaseprogression model using neural networks
Ryota Jin 1

Hideki Yoshioka 1, 2

Akihiro Hisaka 1ryota-j@chiba-u.jp

ysokhdk@gmail.com

hisaka@chiba-u.jp

1

2

Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences,
Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8675, Japan.
Pharmaceuticals and Medical Devices Agency, Tokyo, Japan.

Keywords: Disease progression model, Neural network, Biomarker, TensorFlow
In this study, a new method was developed using machine learning to estimate long-term chronic
disease progression over decades from short-term fragmented data typically found in big data or realworld data. The new method breaks through the computational limitations (several thousand subjects
and less than ten biomarkers) of our previous method, the Statistical Restoration of Fragmented Timecourse (SReFT) [1-3], and would be applicable for precision medicine in the future.
Our new method consists of two networks. The first network relocates the short-term observations
over a long-term time axis. The second network estimates the transition of each biomarker. In thispresentation, we tested the validity of the analysis and the ability to reject meaningless biomarkers
as noise appropriately. Simulation results using synthetic data with varying numbers of cases (500
to 5000) and biomarkers (5 to 30) will be discussed. Individual observations were synthesized
assuming a sigmoid function with error and covariate effect. Noise biomarkers were given random
numbers between 0 and 1 as observations. For a neural network library, we used TensorFlow(version 2.8.0). As a result, the present method predicted long-term biomarker transition from shortterm synthesized observations and appropriately judged that the noise biomarkers did not havemeaning throughout the entire period.

[1] Ishida, T.; et al., A Novel Method to Estimate Long-Term Chronological Changes From
Fragmented Observations in Disease Progression., Clin. Pharmacol. Ther., 2019, 105 (2), 436–447.
[2] Kawamatsu, S.; et al., A. Scores of Health-Related Quality of Life Questionnaire Worsen
Consistently in Patients of COPD: Estimating Disease Progression over 30 Years by SReFT with
Individual Data Collected in SUMMIT Trial. J. Clin. Med. 2020, 9 (8), 2676.
[3] Jin, R.; et al., Statistically estimating life-long progression of chronic disease from theinformation of short clinical trials. CBI Annual Meeting 2020. O2-14.

P04-01

Computational search for compounds exhibiting thesame activity from database by molecular fingerprints
and 3D pseudo-atom methods
Mizuki Matsushita1

Yuka Yamashita1

21bs120b@shinshu-u.ac.jp

20bs117j@shinshu-u.ac.jp

Koji Umezawa1,2
koume@shinshu-u.ac.jp

12Grad. Sch. of Sci. & Tech., Shinshu University, Minami-minowa 8304, Kami-ina, Nagano,399-4598, Japan.
Institute for Biomedical Sciences, Shinshu University, Minami-minowa 8304, Kami-ina,
Nagano, 399-4598, Japan.

Keywords: Structure-activity relationship, Ligand-based virtual screening, Berberine
The relationship between chemical substructure and biological activity has attracted
attention in drug discovery. The chemical substructures within a compound can be described as avector of molecular fingerprint. When two compounds exhibit a similar molecular fingerprint,
their activities should be similar. Comparing molecular fingerprints have been widely used toretrieve the same active compounds as a reference compound from chemical database. However,
the molecular fingerprint search contains difficulty in selection of an appropriate method to collect
the targeted compounds accurately. It needs to be resolved that the result of fingerprint searchdepends on a reference compound and its activity.
In this study, a general substructure search has been developed for different biologicalactivities. Substructures were newly defined based on heteroatom connection and aromatic ring.
We used 3D information of substructures for similarity search. The mean positions of
substructures were calculated and were named as pseudo-atomic sites. The similarity was judged
by satisfaction of the pseudo-atomic distances with those of the reference compound. In order toestimate performance of our method, we used the chemical database, LigandBox (T. Kawabata etal., 2013), and true collection of the same active compounds were taken from drug groups in
KEGG DRUG (M. Kanehisa et al., 2021). The conventional fingerprint methods of MACCS keys,
topological (RDKit), Morgan, and Avalon were used, and the similarity was judged by Tanimoto
coefficient, which are implemented in RDKit open-source cheminformatics.
The 3D pseudo-atom method and the conventional fingerprint methods were applied forcollection of compounds in ACE inhibitor, HMG-CoA inhibitor, and β-lactam antibiotic groups.
The accuracy and completeness of collection was evaluated by the receiver operating
characteristic (ROC) curve. The results showed that Morgan fingerprint was most suited in thecase of ACE inhibitor and HMG-CoA inhibitor groups. However, for β-lactam antibiotic group,
Morgan fingerprint showed worst performance. The 3D pseudo-atom method enabled bettersearch among them.
The 3D pseudo-atom method can be straightforwardly applied for natural compounds.
Natural compound generally exhibits various biological activities. Berberine-like compounds
were searched by the reference of berberine. The details will be discussed in the presentation.

P04-02

Analysis of protein-protein dynamical interaction bymolecular dynamics and principal component analysis
Keima Kawada 1
cske21006@g.nihon-u.ac.jp

1

Masanori Yamanaka 1
yamanaka.masanori@nihon-u.ac.jp

Department of Physics, College of Science and Technology, Nihon University, 1 -8 KanndaSurugadai, Chiyoda-ku, Tokyo, 101-8308, Japan

Keywords: Protein interaction, Molecular dynamics, Principal component analysis
The study of protein-protein and protein-ligand interactions is of great importance in the field of
drug discovery. Static analysis and statistical averaging analysis of snapshots of molecular dynamics
trajectories have been carried out in literature. However, there is no analysis of interactions that
preserves the causal relationships of the time series. Generally, the analysis of dynamic properties
is known to be difficult, not only for proteins.
In this study, we propose an analysis method that preserves the causal relationships of time seriesof protein-protein or protein-ligand interactions. Note that this study is not a simple statistical
average over a large number of snapshots cut from a time series. The test target protein is PDBID:
6DKY, which consists of two small ring proteins. The intermolecular interactions between theprotein rings were investigate using molecular dynamics [1] and principal component analysis [2].
The trajectories of the atomic coordinates were determined numeri cally using molecular dynamics
in water media. The total duration of the molecular dynamics is 100ns. The variance -covariancematrices between atoms were calculated from the obtained trajectory. When creating the variance covariance matrices, the sampling interval of the atomic coordinates was systematically varied from100fs to 10ns. These matrices were diagonalized and subjected to principal component analysis.
The results of eigenvalues as a function of the sampling time interval are shown in Fig.1. The
amino acid residues and atoms with interaction estimated from the second principal component
vector among the results of principal component analysis are listed in Table 1. The interacting atoms
are found to depend on the sampling interval. Thus , this result leads to the following important
conclusions: when sampling and taking statistical averages in molecular dynamics, it is meaninglessunless the sampling interval is set to be optimal for the dynamics of the residue or ligand to be
examined.

Fig.1 (left) Eigenvalues of the variance-covariance matrices. Horizontal axis is the samplinginterval of the snapshot of the molecular dynamics. Table 1 (right) Interacting residues and atoms
obtained from second principal component vector.
[1] https://www.gromacs.org[2] K. Peason, Philosophical Magazine 2 (11), 559 (1901).

P04-03

Constructing QSAR models for c-Met inhibitors
Yu-Shi Tian 1 ,yushi-tian@osaka-u.ac.jp

Daisuke Takaya 1 ,Miho Sasaoka 1
miho914.ikgk@gmail.com

Tatsuya Takagi 1 ,Kaori Fukuzawa 1

takaya-d@phs.osaka-u.ac.jp ttakagi@osaka-u.ac.jp fukuzawa-k@phs.osaka-u.ac.jp
1

Graduate School of Pharmaceutical Science, Osaka University, 1 -6 Yamadaoka, Suita, Osaka
565-0871, Japan

Keywords: Quantitative Structure-Property
classification and regression models

Relationships,

c-Met

inhibitors,

anti-tumor,

c-Met is a receptor-type tyrosine kinase (RTK), plays an important role in cell proliferation,
migration and invasion by binding hepatocyte growth factor (HGF), and is involved in normal
embryonic development, wound healing, and tissue regeneration. However, abnormal HGF/Met
signaling pathway activation has been identified in various tumors, including liver, breast, pancreas,
lung, kidney, bladder, ovary, brain, and prostate, and c-Met amplified cell lines show high sensitivity
to c-Met inhibitors. Furthermore, the HGF/c-Met pathway is involved in the acquired resistance to
EGFR tyrosine kinase inhibitors (EGFR-TKI), and it has been reported that c-Met-mediatedacquired resistance can be overcome by concomitant use of c -Met inhibitors in addition to EGFRTKI. Based on these findings, c-Met is expected to be a promising molecular target in cancer therapy,
and inhibitors are being developed worldwide.
We constructed and compared classic QSAR models of inhibitory activities of published c-Met
inhibitors for future novel inhibitor search. Structure information and inhibitory activities of 424
inhibitors were obtained from published articles. First , we constructed classification models using
2D descriptors calculated by Mordred and general machine learning methods (i.e., kernel Support
Vector Classification (kernel SVC), Random Forest Classification (RFC), Logistic Regression (LR)).
The objective variable was binarized as high and low classes per pIC 50 threshold (pIC 50 = 9, 8, and
7). The best accuracy for test sets was obtained as about 84% using kernel SVC, RFC, and pIC 50 =8. We used one-class SVM (OCSVM) to improve the model further by removing outliers.
Consequently, the RFC can give as high accuracy as 83%, with both recall and specificity higher
than 80%. Therefore, we believe that this model can be used to filter possible candidate compounds.
Furthermore, regression models were conducted using both 2D and 3D descriptors and regression
models (i.e., kernel SVR, linear SVR, RFR, and LASSO). 3D descriptors were calculated from thedocking poses of all inhibitors conducted by MOE Dock. Before docking, the structure of the c -Metprotein was obtained from PDB (ID: 3ZXZ), and the ligand PF-04217903 was re-docked to theprotein to check the docking procedure and parameters. An RMSD of 0.6 was obtained from the re docking. Therefore, all the compounds were docked to c-Met to achieve the docking poses that were
further used to calculate 3D descriptors. A two-step explanatory variable selection was conductedusing the generic algorithm SVM (GASVM) and permutation importance (PIMP). Finally, the kernel
SVR showed the best prediction accuracy (R test 2 = 0.603).
In summary, this study focuses on the anti-cancer target c-Met. We collected the previously
reported c-Met inhibitors and constructed classic QSAR models of both classification and regression
models. The accuracy of the models is high, and these models can be used in future initial scr eeningof novel c-Met inhibitors.
Acknowledgments:
The major part of this study was conducted as the master ’s thesis of Ms. Miho Sasaoka, who is now
graduated. Hirose Foundation financially supported this work (Research Grant to Yu -Shi Tian).

P05-01

Molecular Evolution of Peptides and Energy Level
Statistics of Dipeptides and Tripeptides
Masanori Yamanaka 1
yamanaka.masanori@nihon-u.ac.jp

1

Department of Physics, College of Science and Technology, Nihon University, 1-8 KandaSurugadai, Chiyoda-ku, Tokyo, 101-8308, Japan

Keywords: Molecular evolution, Density functional theory, Energy level statistics
Based on the energy level statistics of amino acids [1, 2], dipeptides, and tripeptides, the molecular
evolution of peptides and proteins was discussed. The energy levels of these peptides were
calculated numerically using ab initio methods, and the statistical distributions of the energy levelstatistics were calculated according to the energy level analysis method in random matrix theory.
Specifically, molecular orbitals were calculated by ab initio methods and Kohn -Sham orbitals were
calculated by density functional theory. To improve t he statistical accuracy, we performed a
multipoint calculation on 10000 molecular structures produced via the molecular dynamics
simulation. For the valence orbitals, the energy-level statistics exhibit repulsion, but the universality
in the random matrix cannot be determined. For the unoccupied orbitals, the energy -level statistics
agrees with the critical level statistics which is an intermediate distribution between the Gaussianorthogonal ensemble and the semi-Poisson statistics for all 20 kinds of amino acids and for all 400
dipeptides. The energy level statistics for most molecules were found to be critical statistics.
During the hundreds of millions of years of molecular evolution of proteins, peptides in a state ofcritical statistics are thought to have differentiated int o Poisson statistics and Gaussian orthogonalensemble [1]. The present study [3] suggests this prediction.
[1] G. Vattay, D. Salahub, I. Csabai, A. Nassimi, and S. A. Kaufmann, 2015 J. Phys.: Conf. Ser. 626,
012023.
[2] M. Yamanaka, J. Phys. Soc. Jpn, 86 (2017), 023801.
[3] This work is supported by JSPS KAKENHI, Grant Number 19K21857.

P05-02

KEK GoToCloud project: An Application of Cloud
Computing for Structural Biology Research in KEK
Yusuke Yamada 1

Toshio Moriya 1

yusuke.yamada@kek.jp

toshio.moriya@kek.jp

Toshiya Senda 1
toshiya.senda@kek.jp

1Structural biology research Center, Institute of Materials Structure Science, High Energy
Accelerator Research Organization, 1-1 Oho, Tsukuba, Ibaraki, 305-0801, Japan

Keywords: Cloud computing, Cryo-electron microscope, Macromolecular crystallography
Structural Biology Research Center in High Energy Accelerator Research Organization (KEK)
develops and operates structural biology research facilities such as macromolecular crystallography
beamlines, solution scattering beamlines at synchrotron radiation rings and a cryo-electron
microscope. We develop measurement and data analysis techniques with these facilities and provide
them to the academic and industrial users. The recent advances of methods, especially theautomation of measurements, has made it possible to acquire large amounts of data in a short periodof time, requiring a large amount of computing resources for analysis, which is an urgent issue tobe addressed. We have been focusing on the abundant computing resources and convenience of the
public cloud and has been promoting its utilization for the analysis of measurement data (KEK
GoToCloud project).
Amazon Web Service (AWS) is one of the providers of cloud computing, and they serve a highperformance computing service, AWS ParallelCluster, which enables us to generate a virtualcomputer cluster with many types of computer instances and high-performance shared storage.
Recently we successfully deploy AWS ParallelCluster for data analysis of macromolecular
crystallography and single particle analysis of cryo-electron microscope.
In this presentation, we will introduce our efforts to use cloud computing and show some examples
on macromolecular crystallography and single particle analysis of cryo-electron microscope.

P06-01

Prediction of drug-induced liver injury in silico usinglarge-scale adverse event database
Sarara Doi 1

Kaori Ambe 1

c172035@ed.nagoya-cu.ac.jp

ambek@phar.nagoya-cu.ac.jp

Masahiro Tohkin 1
tohkin@phar.nagoya-cu.ac.jp
1

Graduate School of Pharmaceutical Sciences, Nagoya City University, 3-1
Tanabe-dori, Mizuho-ku, Nagoya-shi, Aichi, 467-8603, Japan

Keywords: Applicability Domain, Drug-Induced Liver Injury, JAPIC AERS , Random Forest,
Synthetic Minority Over-sampling Technique
The risk of drug-induced liver injury (DILI) is difficult to be predicted from data of non-clinicaland clinical trials because some of them are idiosyncratic adverse drug reactions. JAPIC AERS [1]built by cleaning the FDA Adverse Event Reporting System (FAERS) is useful as large-scaleclinical adverse events information database. In this study, we aimed to build a machinelearning model to predict positive or negative of DILI in drugs from chemical structural
information using JAPIC AERS.
We used JAPIC AERS data from the fourth quarter of 1997 to the second quarter of 2020
to extract DILI-positive/negative drugs, which was defined by the Proportional Reporting
Ratios (PRR) method and the number of reports. We calculated the 2D molecular
descriptors of drugs’ chemical structure by alvaDesc [2]. After the feature selection, weconstructed a DILI classification model using Random Forest (RF). To improve theimbalanced data, we applied the Synthetic Minority Over-sampling Technique (SMOTE).
Furthermore, external validation was done using data of DILIrank which lists the DILIpositive drugs [4] to evaluate the performance of our model.
A dataset of 424 positive drugs and 210 negative drugs was created from JAPIC AERS. We
established the machine-learning clarification model to predict the positive or negative of
DILI with a ROC-AUC of 0.8 as the internal validation. With the improvement in data
imbalance by SMOTE, specificity was improved, resulting in better overall performance. To
create a dataset for external validation, 76 positive drugs and 139 negative drugs wereobtained from the DILIrank. The ROC-AUC is over 0.7 for external validation after setting
Applicability Domain (AD) . These results suggested that we successfully constructed a modelwith high generalization performance.
[1] https://www.japic.or.jp[2] Alvascience, 2020, https://www.alvascience.com[3] Chawla NV.; et al., SMOTE: synthetic minority over-sampling technique, J Artif Intell Res,
2002, 16, 341-378[4] Chen M.; et al., FDA-approved drug labeling for the study of drug-induced liver injury, Drug Discov
Today, 2011, 16(15–16), 697–703.

P06-02

Characterizing Predictive Effects of Autophagy Agents on
the Early Stages of Embryo Development
Kengo Matsugo
204031_pf@yok.hamayaku.ac.jp

Kohsuke Hayamizu
k.hayamizu@hamayaku.ac.jp

Yuumi Namiki
194018_md@yok.hamayaku.ac.jp

Hideko Sone
hideko.sone@yok.hamayaku.ac.jp

Yokohama Univ. Pharm., 601 Matano Totsuka Yokohama 245 -0066, Japan

Keywords: autophagy; neuronal development; toxicity prediction; teratogens
Autophagy is the major cellular pathway to degrade dysfunctional organelles and protein
aggregates during the early stage development. Especially, neurons depend largely on autophagy tomaintain cell homeostasis to get rid of damage d. In this way, as the understanding of autophagy
progresses, the modulators of the autophagy pathway are being organized. However, underchemical-induced stress conditions, changes in survival mechanisms of cell functions are unknown.
Therefore, whether autophagy modulators induces dysfunction of this pathway should be
investigated since the dysfunction of this process contributes to the pathology of many humandiseases. In this study, we characterized predictive effects of autophagy modulators with known
toxic agents and teratogens based on the current knowledge of the different pathways, molecularmechanisms of mammalian autophagy. [1-6].
[1] Klionsky, D.J. Guidelines for the use and interpretation of assays for monitoring autophagy(4th edition)1. Autophagy. 2021 17:1-382.
[2] Yamane J, et al., StemPanTox: A fast and wide-target drug assessment system for tailor-madesafety evaluations using personalized iPS cells. iScience. 2022 Jun 6;25(7):104538.
[3] Yamane J, et al., Prediction of developmental chemical toxicity based on gene networks ofhuman embryonic stem cells. Nucleic Acids Res. 2016, 44, 5515-5528.
[4] Filippone, A. et al., The contribution of altered neuronal autophagy to neurodegeneration.
Pharmacol Ther. 2022, 238,108178.
[5] Tang, F et al., The Role of Vti1a in Biological Functions and Its Possible Role in Nervous System
Disorders. Front Mol Neurosci. 2022, 15, 918664.
[6] Andrea, K. H. et al., Autophagy in Neurons Annu Rev Cell Dev Biol, 2019, 35, 477-500.

P06-03

Application of Deep Learning to the Evaluation of Seizurelike Behavior Using Zebrafish
Mizuho Wakasa 1

Riki Kajiwara 2

Kaori Abe 1

Wakasa.Mizuho@otsuka.jp
12

Department of Investigative Toxicology, Nonclinical Research Center, Tokushima
Research Institute, Otsuka Pharmaceutical Co., Ltd., 463 -10 Kagasuno, Kawauchi-cho
Tokushima 771-0192, Japan
Department of Research Management, Otsuka Pharmaceutical Co., L td., 463-10
Kagasuno, Kawauchi-cho Tokushima 771-0192, Japan

Keywords: Toxicity prediction，Deep Learning，Multi-animal tracking，Central Nervous System
Drug-induced convulsive seizures are a serious adverse event and drug evaluation is usually
carried out using behavioural indicators in laboratory animals such as rodents. However, theseevaluation methods use large numbers of animals and require large amo unts of test substance,
resulting in low throughput. While tests using laboratory animals provide a lot of useful information,
they are also considered to be an area for improvement in terms of animal welfare, speed of researchand development and cost. To address this issue, a test method using zebrafish, which can beevaluated with a small amount of test substance ( -µg) and can contribute to the promotion of the
3Rs, has been reported using the amount of behaviour immediately after drug addition as anindicator. However, in the previously reported evaluation system, the presence or absence ofconvulsions is finally determined by visual observation by researchers, which causes problems suchas inconsistency in evaluation, reduced throughput and difficulty i n multi-tracking. Therefore, inthis study, we aimed to solve the above issues by introducing AI into the evaluation of seizure -likebehaviour using zebrafish and automating some of the tasks.
In this presentation, an overview of automated tracking for each designated site using
DeepLabCut[1-3], extraction of behavioural parameters that change in relation to compound
administration, and evaluation of a seizure -like behaviour classification model using the
behavioural parameter dataset will be presented.
[1] Lauer, J.; et al., Multi-animal pose estimation and tracking with DeepLabCut, BioRxiv, 2021.
[2] https://github.com/DeepLabCut/DeepLabCut[3] http://www.mackenziemathislab.org/deeplabcut

P07-01
Artificial Intelligence to Accelerate New Drug Discovery:
Target identification and drug discovery by data-driven
approach and experimental validation in Idiopathic Pulmonary
Fibrosis (IPF)
Mari N Itoh 1

Yayoi Natsume-Kitatani 1

mari@nibiohn.go.jp

Jun Adachi 3
jun_adachi@nibiohn.go.jp

Yoshito Takeda 4
yoshito@imed3.med.osaka-u.ac.jp

Masataka Kuroda 1,2

natsume@nibiohn.go.jp

m-kuroda@nibiohn.go.jp

Yi-An Chen 1

Kenji Mizuguchi 1

chenyian@nibiohn.go.jp

kenji@nibiohn.go.jp

Atsushi Kumanogoh 4
kumanogo@imed3.med.osaka-u.ac.jp

Naonori Ueda 5
naonori.ueda@riken.jp

1

AI Center for Health & Biomedical research, Bioinformatics Project, National Institutes of Biomedical
Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka, 567-0085, Japan
2
Discovery Technology Laboratories, Mitsubishi Tanabe Pharma Corporation, 1000, Kamoshida -cho, Aobaku, Yokohama, Kanagawa 227-0033, Japan
3
Laboratory of Proteome Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7 6-8 Saito-Asagi, Ibaraki, Osaka, 567-0085, Japan
4
Osaka University Graduate School of Medicine, 2 -2 Yamada-oka, Suita, Osaka, 565-087, Japan
5
RIKEN Center for Advanced Intelligence Project, RIKEN, 1 -4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027,
Japan

Keywords: Pathway analysis, Exosome Proteomics, Target validation
With the support of PRISM (Public/Private R&D Investment Strategic Expansion PrograM) project, we
aim to develop the novel strategy such as data-driven target identification and drug discovery based on
patient data.
We have created a dataset linking proteome data in serum exosome s obtained by state-of-the-art data
independent acquisition (DIA) and structured electronic medical record (EMR) data for each patient.
Furthermore, a novel algorithm named subset binding (SB) made it possible to extract as many featureslinked to each other as possible from the two different pieces of information.
In this conference, we will report the results of using this algorithm to search for novel drug targets for
idiopathic pulmonary fibrosis (IPF), an intractable disease with poor prognosis, as well as the results ofpathway analysis and experimental validation as follows,
1) 20 proteins that are strongly connected with medical records, including novel molecules not known
to be associated with IPF, were detected. They were up-regulated in the fibrotic areas of patientlungs.
2) Interpreted and analyzed these 20 proteins using bioinformatics methods, utilizing Ingenuity
Pathway Analysis (IPA) and TargetMine, to investigate PPI networks and upstream master regulators.
3) Ponatinib, one of the candidates which inhibits upstream master regulators of the detected IPFrelated proteins, was experimentally validated in an epithelial-mesenchymal transition of human
airway epithelial cells.
Conclusion: The involvement of the molecules identified by SB in the pathogenesis of IPF and theirplausibility as drug targets were confirmed from multiple perspectives.

P07-02

Five cluster groups found in the health state spaceof 96,093 population
Satoshi Nagaie 1
nagaie@megabank.tohoku.ac.jp

Ryosuke Ishiwata 1
rishiwata@megabank.tohoku.ac.jp

1

Satoshi Mizuno 1
samizuno@med.tohoku.ac.jp

Soichi Ogishima 1
ogishima@megabank.tohoku.ac.jp

Dept. of Informatics for Genomic Medicine, Tohoku Medical Megabank Organization, Tohoku
University, Tohoku Medical Megabank Organization Bldg 7F room #736, Seiryo 2-1, Aoba-ku,
Sendai, Miyagi, 980-8573

Keywords: health state space, dimensional reduction, real-world data
What is health? Can the degree of health be quantified? While it is important to look at clinicalvalues individually, by looking at those multidimensional values as a whole, it is possible tounderstand the extent to which the subject deviates from a "healthy" population, which may
contribute to improving the subject's health status. Therefore, the purpose of this study is to provide
a quantitative understanding of quantified health status as a "health state space" that represents thehealth status of the subjects.
In this study, we analyzed 4 years (2014-2017) health checkup data which consists of 42 variables
without missing values (17 clinical variables, 25 lifestyle variables) in 96,093 populations (64,128
males and 31,965 females) from the JMDC database. At first, dimensional reduction was conductedby using PCA and UMAP to define variables that span the health state space. In the health state
space, we then detected clusters by conducting DBSCAN. Finally, we categorized detected clusters
into cluster-groups by analyzing the adjacency matrix of cluster networks.
As a result, we detected twelve clusters in the health state space, and we found that the health state
space of populations consists of five cluster-groups: one healthy cluster-group A and four clustergroups B-E with health problems. Cluster-group B had features related to diabetes metabolism withhigher HbA1c and glucose levels as one moved away from the center of the health state space.
Cluster-group C had features related to obesity, lipid abnormalities, liver function abnormalities,
and daily alcohol consumption. Cluster-group D was characterized by red blood cell-related
abnormalities such as very low red blood cell count. Cluster-group E was characterized byhypertension, abnormal liver function, and a history of cerebrovascular and cardiovascular disease.
Regarding transitions between cluster groups, transitions from healthy cluster-group A to each of
non-healthy cluster-groups B-E were observed, but not among cluster group B-E. To further quantify
the degree of health, we are planning to stratify health cluster-group A and analyze it toquantitatively assess health status within the health state space.

P08-01

Proposal of a simple pose descriptor for prediction ofcorrect docking poses by machine learning approaches
Kentaro Kawai 1

Daiki Fujioka 1

Daiki Minami 1

Yukiko Karuo 1

Shinya Nakamura 2

Masaaki Omote 1

kentaro.kawai@pharm.setsunan.ac.jp

Atsushi Tarui 1
1
2

Kazuyuki Sato 1

Faculty of Pharmaceutical Sciences, Setsunan University, 45-1 Nagaotoge-cho, Hirakata,
Osaka 573-0101, Japan
Faculty of Pharmaceutical Sciences, Kindai University, 3-4-1 Kowakae, Higashi-osaka,
Osaka 577-8502, Japan

Keywords: Ligand docking, Pose prediction, Machine learning
To facilitate SBDD, it is necessary to understand the correct binding pose of a compound to thetarget protein, but it is difficult to predict the exact binding pose based on docking score alone.
Therefore, we tried to predict the correct binding pose using various machine learning methods, i.e.,
SVM, random forest, and lightGBM [1]. a dataset was generated using binding poses generated by
AutoDock Vina [2] and rDock [3], then the RMSD with the crystal structure ligand values less than
2.5 Å were considered as correct binding poses. Here, we compared two methods for numericalexpression of binding poses, PLIP [4] and LCP [5]; LCP was implemented by us and is a simple
binding pose representation method, which is calculated based on the atom type and coordinates ofthe ligand atoms.
As a test case, xanthine oxidase was used in this study, and 21 different complex structures wereobtained from the PDB, each of which was self-docked to obtain a maximum of 120 different
binding poses per complex. The binding poses were represented numerically using PLIP and LCP
expressions. The results showed that random forests gave better results for PLIP, and lightGBM
gave better results for LCP, respectively. In both cases, the prediction of binding poses for nucleic
acid derivatives was found to be poor. We considered this was attributed to their small-sizedmolecules with many functional groups capable of forming hydrogen bonds.
References:[1] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T. Y. Liu, NIPS’17: Proceedingsof the 31st International Conference on Neural Information Processing Systems , 2017, 3149–
3157.
[2] Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a newscoring function, efficient optimization, and multithreading. J Comput Chem. 2010, 31, 455-461.
[3] Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S, Schmidtke P,
Barril X, Hubbard RE, Morley SD. rDock: a fast, versatile and open source program for docking
ligands to proteins and nucleic acids. PLoS Comput Biol. 2014, 10, e1003571.
[4] Salentin S, Schreiber S, Haupt VJ, Adasme MF, Schroeder M. PLIP: fully automated protein ligand interaction profiler. Nucleic Acids Res. 2015, 43, W443-7.
[5] Kawai K, Asanuma Y, Kato T, Karuo Y, Tarui A, Sato K, Omote M. LCP: Simple Representationof Docking Poses for Machine Learning: A Case Study on Xanthine Oxidase Inhibitors. Mol
Inform. 2022, 41, e2100245.

P08-02

Global Assessment of Substituentson the Basis of Analogue Series
Kosuke Takeuchi 1

takeuchi.kosuke.pm@daiichisankyo.co.jp

Ryo Kunimoto 1

kunimoto.ryo.si@daiichisankyo.co.jp

Jürgen Bajorath 2

bajorath@bit.uni-bonn.de1
2

R&D Division, Daiichi Sankyo Co., Ltd., 1-2-58 Hiromachi, Shinagawa-ku, Tokyo 140-8710,
Japan
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology &Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Bonn, Friedrich-HirzebruchAllee 6, Bonn, D-53113, Germany

Keywords: Substituent Analysis, R-group, Functional group, Fragmentation, Analog series
While bioisosteric replacements have been extensively investigated, comprehensive analyses of R/functional groups have thus far been rare in medicinal chemistry. We introduce a new analysis concept
for the exploration of chemical substituent space that is based upon bioactive analogue series as a source.
From ∼ 24,000 analogue series, more than 19,000 substituents were isolated that were differently
distributed. A subset of ∼400 substituent fragments occurred most frequently in different structuralcontexts. These substituents contained well-known R-groups as well as novel structures. Substitutionsite-specific replacement and network analysis revealed that chemically similar substituents
preferentially occurred at given sites and identified intuitive substitution pathways that can be exploredfor compound design. Taken together, the results of our analysis provide new insights into substituent
space and identify preferred substituents on the basis of analogue series.

[1] Takeuchi, K.; Kunimoto R.; Bajorath J. Global Assessment of Substituents on the Basis of
Analogue Series, J. Med. Chem. 2020, 63, 15013–15020.
[2] Lewell, X. Q.; Judd, D. B.; Watson, S. P.; Hann, M. M. RECAP Retrosynthetic Combinatorial
Analysis Procedure: A Powerful New Technique for Identifying Privileged Molecular Fragmentswith Useful Applications in Combinatorial Chemistry. J. Chem. Inf. Comput. Sci. 1998, 38, 511−
522.
[3] Naveja, J. J.; Vogt, M.; Stumpfe, D.; Medina-Franco, J. L.; Bajorath, J. Systematic Extractionof Analogue Series from Large Compound Collections Using a New Computational Compound–Core Relationship Method. ACS Omega 2019, 4, 1027– 1032.

P08-03

Validation of low-resolution protein crystal structuresusing deep learning
Takaaki Kuribayashi 1
kuribayashi-takaaki@mki.co.jp

Akiko Kashima 2
kashima.akiko@mb.mt-pharma.co.jp

Ikuko Miyaguchi 2
miyaguchi.ikuko@mv.mt-pharma.co.jp

Shigeyuki Matsumoto 3
sigeyuki.matsumoto@riken.jp

Hiroaki Hata 1
hata-hiroaki@mki.co.jp

Masateru Ohta 4masateru.ota@riken.jp

Mitsunori Ikeguchi 5
ike@tsurumi.yokohama-cu.ac.jp
1
2

3
4

5

Mitsui Knowledge Industry Co., LTD. 2-5-1 Atago, Minato-ku, Tokyo 105-6215, Japan
Mitsubishi Tanabe Pharma Co., LTD. 1000 Kamoshida, Aoba -ku, Yokohama, Kanagawa
227-0033, Japan
Graduate School of Medicine, Kyoto University, Kyoto 606 -8507, JapanRIKEN Center for Computational Science, 1 -7-22, Suehiro-cho, Tsurumi-ku, Yokohama
230-0045, Japan
Graduate School of Medical Life Science, Yokohama City University, 1 -7-29, Suehiro-cho,
Tsurumi-ku, Yokohama 230-0045, Japan

Keywords: Electron density, Protein structure, 3D-CNN, X-ray crystallography
Many protein structures have been determined by X-ray crystallography. High-resolution data
(electron density map) is required to determine atomic coordinates with high accuracy. For
determining side chain orientations, the resolution of 2 Å or higher is often necessary. However,more than half of registered entities in the Protein Data Bank (PDB) have less than 2 Å resolution[1], so that it brings difficulties in drug development or molecular simulation studies .
We therefore started to develop a method named Quality Assessment based on an Electron densitymap (QAEmap) [2], which is applied 3D-convolutional neural network (3D-CNN) to evaluate local
protein structures determined from such a low-resolution data.
Our method estimates how well the structure fits the putative high -resolution structure bycalculating a correlation between the coordinate structure to be evaluated and the electron density
map of the correct structure in amino acid units. We expect that QAEmap enables protein structuresfrom low-resolution data to be utilized in many studies.
[Acknowledgement] This research was carried out as activities of Life Intelligence Consortium (LINC).

[1] Burley, S.K. et al. Protein Data Bank: The single global archive for 3D macromolecular structure
data. Nucleic Acids Res. 47, D520-D528 (2019).
[2] Miyaguchi, I. et al. Machine learning to estimate the local quality of protein crystal structures.
Sci. Rep. 11, 1–13 (2021).

P08-04

Improvement of Compound-Protein Interactions Prediction
with Semi-Supervised Learning
Takuto Koyama 1

Shigeyuki Matsumoto 1

koyama.takuto.82j@st.kyoto-u.ac.jp

Hiroaki Iwata 1
iwata.hiroaki.3r@kyoto-u.ac.jp

1matsumoto.shigeyuki.4z@kyoto-u.ac.jp

Ryosuke Kojima 1
kojima.ryosuke.8e@kyoto-u.ac.jp

Yasushi Okuno 1
okuno.yasushi.4c@kyoto-u.ac.jp

Graduate School of Medicine, Kyoto Univ., 53 Kawahara-cho, Shogoin, Sakyo-ku,
Kyoto, 606-8507, Japan

Keywords: Compound-protein interactions, Deep learning, Data augmentation, Semi-supervised
learning
Accurate prediction of Compound-Protein Interactions (CPIs) can accelerate the process of drug
discovery. Along with the recent development and expansion of databases associated with CPIs,
supervised machine-learning frameworks, especially using deep learning techniques, have become
one of the powerful approaches to CPIs prediction [1, 2]. However, the differences in the sample
sizes and the affinity distributions cause non-negligible data imbalance for each target protein in
the database, significantly impairing the performance of the model trained with the dataset.
To solve the problem, we here propose a semi-supervised learning framework based on a data
augmentation approach. We first constructed a deep learning model to predict the CPI activities
using a training dataset derived from the ChEMBL database. The model has a multimodal
architecture of both chemical structures and protein sequences with the corresponding activity labels
as “positive” or “negative” according to a threshold of 1uM. In the present semi-supervised
framework, we adopted a bootstrap-based approach that iteratively generates pseudo-labels forcompound-protein pairs without a priori interaction information and refines the model parameters.
The data imbalance in the generated datasets was shown to be progressively alleviated, and further,
the finally constructed model outperformed the initial model which was trained only with the known
CPIs data. The present results indicate that semi-supervised learning is a useful approach foraugmenting data in the extremely vast CPI space, providing a clue to realize the practical in-silico
drug discovery using a deep learning model.
[1] Hamanaka, M., Taneishi, K., Iwata, H., Ye, J., Pei, J., Hou, J., & Okuno, Y. (2017). CGBVS‐DNN: Prediction of Compound ‐ protein Interactions Based on Deep Learning. Molecular
informatics, 36(1-2), 1600045.
[2] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., & Okuno, Y. (2020). kGCN: a graphbased deep learning framework for chemical structures. Journal of Cheminformatics, 12(1), 1-10.

P08-05

Search for allosteric chaperones forlysosomal acid α-glucosidase

Manami Yamazaki 1

Izumi Nakagome 1

ml21132@st.kitasato-u.ac.jp

Nobutada Tanaka 1
tanakan@pharm.kitasato-u.ac.jp

nakagomei@pharm.kitasato-u.ac.jp

Yuna Shimadate 2

Atsushi Kato 2

d2162304@ems.u-toyama.ac.jp

kato@med.u-toyama.ac.jp

1

Graduate School of pharmaceutical science , Kitasato University, 5-9-1 Shirokane, Minato,
Tokyo 108－8641, Japan2
Department of Hospital Pharmacy, University of Toyama, 2630 Sugitani, Toyama 930-0194,
Japan

Keywords: Pompe disease, N-acetylcysteine, in silico fragment mapping
Pompe disease is a congenital metabolic disorder caused by genetic mutation of acid α-glucosidase(GAA), which breakdowns glycogen in lysosomes. GAA deficiency in Pompe disease results inaccumulation of glycogen and myopathy. The approved treatment is enzyme replacement therapy(ERT) with recombinant human GAA, but there have been report s of cases in which treatment islimited by factors such as the emergence of antibodies. Therefore, as an alternative treatment to
ERT, pharmacological chaperone therapy, in which a low-molecular-weight compound is bound toan enzyme that has become unstable due to gene mutation, to suppress the degradation of the enzyme,
has attracted attention [1]. N-acetylcysteine (NAC) has been reported as an allosteric chaperone for
GAA, but there is no allosteric chaperone currently used as a therapeutic drug for Po mpe disease[2]. Therefore, in this study, with the aim of identifying novel allosteric chaperones for GAA, virtual
screening was performed by combining the in silico fragment mapping method developed by ourlaboratory with the docking method. The chaperone activity of the resulting representative
compounds against GAA was measured.
[1] Roig-Zamboni, V., Cobucci-Ponzano, B., Iacono, R. et al. Structure of human lysosomal
acid α-glucosidase–a guide for the treatment of Pompe disease. Nat Commun 8, 1111 (2017).
[2] Caterina Porto et al ., Molecular Therapy 20, 2201-2211 (2012).

P08-06

Prediction of pharmacological activity by deep learning
using skeletal formula images
Satoshi Ito 1

Takafumi Nakanishi 1,2

Masatsugu Hashimoto 1

g191tg2001@dhw.ac.jp

takafumi@dhw.ac.jp

mshashimoto@dhw.ac.jp

1

Digital Hollywood University Graduate School, Ochanomizu Solacity Academia 3F/4F, 4-6
Surugadai, Kanda, Chiyoda-ku, Tokyo,101-8219, Japan2

Musashino University, 3-3-3, Ariake, Koto-ku, Tokyo, 135-8181, Japan

Keywords: AI Drug Discovery, Activity/Drug Efficacy (Potency) Prediction, in silico Screening
With machine learning to predict the pharmacological activity of compounds, molecular fingerprint
which express the presence of specific molecular structure is used[1]. Since molecular fingerprint
focuses on the substructure of a compound, it is presumed to contain less information than the entire
structure of the compound. On the other hand, molecular graph which the molecular structure is
regarded as a graph and molecular image can be used to represent the entire molecular structure[2].
In attempts to use molecular images, there is a report of case which skeletal formula images areused to predict compound rate constants[3]. And the case which prediction pharmacological activity
by deep learning using skeletal formula images is not reported. Therefore, in addition to molecularfingerprint, which focus on partial structures, we confirmed the efficacy of predicting
pharmacological activity using skeletal formula images that represent the entire molecule.
Assay data were extracted from ChEMBL and data were split at a ratio of 8:2 for training andtesting. SMILES strings were converted into extended-connectivity fingerprint of diameter4(ECFP4), Molecular Access System(MACCS) keys and skeletal formula images with RDKit. The
pharmacological activity is discriminated from pIC 50 (-logIC 50 ). For predictions in ECFP4 and
MACCS keys, we used Support Vector Machine(SVM), Random Forest(RF), k-Nearest Neighbors
algorithm(k-NN), and Multi layer Perceptron(MLP) as machine learning model. And we used Visual
Geometry Group 16(VGG16), Dense Convolutional Network 121(DenseNet121), both areconvolutional neural network(CNN) based architectures, and the model provided by AutoKeras[4],which is Automated Machine Learning(AutoML) library, for prediction with skeletal formula
images.
We got the results that were 92.5% accuracy, 95.5% recall, and ROC-AUC 0.965 when classified
by SVM with ECFP4 using assay data with Dipeptidyl Peptidase 4 (DPP4) as the target protein. The
results for the skeletal formula images were 79.5% accuracy, 93.4% recall, and ROC-AUC 0.836
for the model provided by AutoKeras.
We would like to continue to evaluate aspects other than classification performance.
[1] Harinder, S.; et al., QSAR based model for discriminating EGFR inhibitors and non-inhibitors
using Random forest, Biology Direct, 2015, 10, 10.
[2] Dejun J.; et al., Could graph neural networks learn better molecular representation for drug
discovery? A comparison study of descriptor-based and graph-based models, Journal ofCheminformatics, 2021, 13, 12.
[3] Shifa, Z.; et al., Molecular image-convolutional neural network (CNN) assisted QSAR models
for predicting contaminant reactivity toward OH radicals: Transfer learning, data augmentation andmodel interpretation, Chemical Engineering Journal, 2021, 408, 127998.
[4] AutoKeras: An AutoML system based on Keras. https://autokeras.com/. (2022.08.08)

P08-07

AI-AAM. Downsizing and scaffold hopping from a peptide
to small-molecule inhibitors
Shino Ohira 1

Jun Nakabayashi 1

shino.ohira@fujifilm.com

jun.nakabayashi@fujifilm.com

Kyosuke Tsumura 1

Issei Doi 1

kyosuke.tsumura@fujifilm.com
1

issei.doi@fujifilm.com

Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minamiashigara-shi,
Kanagawa 250-0193, Japan

Keywords: AI Drug Discovery, LBDD, de novo design, PPIs, AI-AAM
The investigation of unexplored protein-protein interactions (PPIs) for diseases with an unmet
medical need is highly demanding [1]. An interest to approach small molecule PPI inhibitor (MW <500 Da) with high cell permeability instead of peptides and antibodies is increasing, but systematic
methodology for finding such compounds has not yet been established.
We developed a peptide downsize and scaffold hopping methodology using AI-Amino Acid
Mapping (AI-AAM) that could find and design compounds with desired binding affinities to a target
starting from a known active compound with modifying its original core structure based oninteractions between the ligand and the set of amino acids [2]. Using the methodology, we designed
a novel compound starting from a cyclic peptide with 14 amino acids, Peptide-71 (MW = 1.8 kDa)
binding to PD-L1 [3]. Whereas the starting compound Peptide-71 had high in vitro binding affinity
with low MDCK-II permeability, we experimentally confirmed that the computed compound
exhibited binding efficiency index (BEI= pKi/Mw×1000) twice as high as the original compoundand high permeability which was suitable for oral absorption (Fig. 1).
Peptide-71

AAM

Binding site of
Peptide-71 to PD-L1

Ki(μM)
BEI(pKi/Mw×1000)
Permeability(10-6 cm/s)

0.004
4.7
0.08±0.02

A small-molecule
inhibitor

Partial AAM

399.2
11.1±0.8

Fig. 1 Downsizing and scaffold hopping from Peptide-71 to a novel small-molecule inhibitor via AAM[1] Cunningham, A. D.; et al., Peptides and peptidomimetics as regulators of protein-protein
interactions, Curr. Opin. Struct. Biol. 2017, 44, 59.
[2] K. Tsumura.; et al., A newly developed method based on AI-oriented amino acid interaction
mapping (AI-AAM) for efficient virtual scaffold hopping, CBI Annual Meeting 2018, P2-02.
[3] K Magiera-Mularz.; et al., Bioactive Macrocyclic Inhibitors of the PD-1/PD-L1 Immune
Checkpoint, Angew. Chem. Int. Ed. 2017, 56, 1.

P08-08

Graph based property and affinity prediction using
open-source tool and cloud platform
Akitoshi Okada 1
akitoshi.okada@jt.com

1

2

Hiroyuki Ogawa 1
hiroyuki.ogawa@jt.com

Yohei Nakayama 2

Tatsuya Arai 2

nak@amazon.com

araitats@amazon.com

Chemical Research Laboratories, Central Pharmaceutical Research Institute,Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
Amazon Machine Learning Solutions Lab, Amazon Web Services Inc.,
2795 Augustine Dr. Santa Clara, CA 95054

Keywords: Graph neural networks, Machine Learning, Molecular Property prediction
Molecular property prediction using machine learning model is widely used in the pharmaceuticalindustry [1]. Graph neural network (GNN) is one of the methods to construct such molecularproperty prediction model. GNN is becoming a popular method since the model prediction accuracy
can outperform the other prediction methods [2], although the descriptor -based models can still be
the first choice [3] depending on the dataset available to construct the model. It is therefore
becoming more common to add GNN approach to other exi sting methods and verify the mostsuitable model for the target property prediction.
DGL-LifeSci [4], is an open-source python based GNN package and several GNN approach isavailable within the package. It is based on Deep Graph Library [5] and can be used to efficiently
construct graph based molecular prediction model s. With the increasing number of dataset available,
computational cost can become a bottleneck in model construction and comparison. Therefore,
cloud platform such as Amazon Web Service (AWS) can also be useful.
We applied DGL-Lifesci to our internal dataset to construct several properties and affinity
prediction models. Computation was performed on AWS to maximize the calculation speed. There
were several promising targets where GNN outperformed the other methods, and we will discuss
the efficiency of both the computation and the model performance of this approach.
[1] R.S.K. Vijayan, Jan Kihlberg, Jason B. Cross, Vasanthanathan Poongavanam . Enhancing
preclinical drug discovery with artificial intelligence, Drug Discovery Today, 2022, 27, 4, 967984[2] Oliver Wieder, Stefan Kohlbacher, Mélaine Kuenemann, Arthur Garon, Pierre Ducrot, Thomas
Seidel, Thierry Langer, A compact review of molecular property prediction with graph neuralnetworks, Drug Discovery Today, 2020, 37, 1-12
[3] Dejun Jiang, Zhenxing Wu, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Zhe Wang, Chao Shen,
Dongsheng Cao, Jian Wu, Tingjun Hou, Could graph neural networks learn better molecular
representation for drug discovery? A comparison study of descriptor -based and graph-basedmodels, Journal of Cheminformatics 2021, 13, 12[4] Mufei Li, Jinjing Zhou, Jiajing Hu, Wenxuan Fan, Yangkang Zhang, Yax in Gu, George Karypis.
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science , ACS Omega
2021, 6, 27233−27238[5] Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma,
Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, Zheng Zhang . A GraphCentric, Highly-Performant Package for Graph Neural Networks , 2019 arXiv:1909.01315

P08-09
Development of a screening system for RNA-binding
small molecules based on large-scale information ofinteractions between RNAs and a fluorescence indicator
Ryosuke Nagasawa 1,2 , Kazumitsu Onizuka 1,2 , Kaoru R. Komatsu 4 ,nagasawa.ryosuke.p4@dc.tohoku.ac.jp, kazumitsu.onizuka.d7@tohoku.ac.jp, krk@xforestx.com,

Emi Miyashita 3,4 , Mamiko Ozawa 1 , Hirohide Saito 3 ,emi.miyashita@xforestx.com, mamiko.ozawa.c7@tohoku.ac.jp, hirohide.saito@cira.kyoto-u.ac.jp

Fumi Nagatsugi 1,2

fumi.nagatsugi.b8@tohoku.ac.jp
1

IMRAM Tohoku Univ., Katahira 2-1-1, Aoba-ku, Sendai, Miyagi, 980-8577, Japan,

2

Department of Chemistry, Tohoku Univ., Aoba-ku, Sendai, Miyagi 980-8578, Japan,

3

CiRA Kyoto Univ., 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan,

4

xFOREST therapeutics, 448-5 Kajiicho, Kamigyo-Ku, Kyoto, 602-0841, Japan

Keywords: RNA-binding small molecules, High-throughput screening, FID assay
RNA-binding molecules are attractive targets for their potential as therapeutic agents for generelated diseases and infections. Fluorescent indicator displacement (FID) assay is a method to findnovel RNA binding molecules. Light-up properties of RNA-binding small molecules enable us to
conduct a high-throughput screening by FID assay. For example, Nishizawa’s group found TO-PRO3 as a powerful fluorescent indicator, and showed an example of FID assay for the bacterial A-site
RNA.[1] However, RNA targets that have been reported in FID assay are still limited, it is essential
to investigate the binding affinity and selectivity of such chemical probes toward various RNAs inorder to further expand the target range.
A new method, FOREST (folded RNA element profiling with structure library), was previously
developed for the large-scale analysis of protein-RNA interactions.[2] We have developed largescale analysis to investigate the interactions of light-up probes with the RNA structure library (3000
structures form pre-miRNA loop, virus RNA and repetitive RNA) by applying FOREST to small
molecules so far.
In this study, we utilized
FOREST to obtain the
RNA-binding informationof fluorescence indicators,
thiazole orange (TO) typeprobes, that are known aslight-upprobes(Fig).
Fromtheobtained
information, we selectedpairs of a target diseaserelated
RNA
and
a
fluorescenceindicator
which strongly binds to thetarget RNA for FID assay.
Fig. High-throughput screening system based on the database
As a result of the FID assay,
we identified several RNA-binding small molecules. In this presentation, we will report our method,
the analytical results and FID assay based on the obtained information in detail.
[1] S. Nishizawa, et al., Chem.Commun. 2019, 55, 3183.
[2] H. Saito, et al., Nat. Commun. 2020, 11, 6275.

P08-10

Reducing the Data Bias in the PDBbind Database1

Nozomu Yamazaki 1

Takashi Ishida 1

yamazaki@cb.cs.titech.ac.jp

ishida@cs.titech.ac.jp

Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan

Keywords: Protein-Ligand Interaction, Binding Affinity Prediction, Drug Discovery
Prediction of protein-ligand binding affinities using structural information plays an essential rolein drug discovery. Deep learning models based on 3D convolutional neural networks (3DCNN), orgraph neural networks (GCN) have been proposed in recent years [1-2], and many of them use the
PDBbind database for the dataset. However, in the PDBbind database, similar ligands tend to have
similar binding affinities. Therefore, when a simple random split is used, the binding affinity canbe predicted from the ligand structure alone. The same is true when using protein structures.
Therefore, existing research using the PDBbind database may be overestimating its prediction
accuracy. Here, we constructed a data split that reduces the effect of such bias by performing
similarity clustering based on protein sequences and ligand molecular fingerprints.
To investigate whether the bias is reduced, we created a baseline model using 3DCNN and GCN
to compare the proposed data split with a random split. We also constructed datasets containing
ligand structure alone or protein structure alone to confirm whether the model is learning fromprotein-ligand interactions.
[1] Jiménez J.; et al., K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3DConvolutional Neural Networks, J. Chem. Inf. Model, 2018, 58, 2, 287–296[2] Jones D.; et al., Improved Protein–Ligand Binding Affinity Prediction with Structure-Based
Deep Fusion Inference, J. Chem. Inf. Model, 2021, 61, 4, 1583–1592[3] Liu Z.; et al., Forging the Basis for Developing Protein–Ligand Interaction Scoring Functions,Acc. Chem. Res., 2017, 50, 2, 302–309[4] Yang, J.; et al., Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions
Lack of Sufficiently Large and Unbiased Datasets, Front. Pharmacol., 2020, 11, 69

P08-11

Estimation of Interaction Mechanism
in Compound-Protein Interaction Prediction
Using Interpretable Deep Learning
Sota Inoue 1

Shoichi Ishida 2

Hiroaki Iwata 1

inoue.sota.65r@st.kyoto-u.ac.jp

ishida.sho.nm@yokohama-cu.ac.jp

iwata.hiroaki.3r@kyoto-u.ac.jp

Shigeyuki Matsumoto 1

Ryosuke Kojima 1

Yasushi Okuno 1

matsumoto.shigeyuki.4z@kyoto-u.ac.jp

kojima.ryosuke.8e@kyoto-u.ac.jp

okuno.yasushi.4c@kyoto-u.ac.jp

12

Graduate School of Medicine, Kyoto Univ., 53 Kawahara-cho, Syogoin, Sakyo-ku, Kyoto,606-8507, Japan
Graduate School of Medical Life Science, Yokohama City Univ., 1-7-29 Suehiro-cho,
Tsurumi-ku, Yokohama, 230-0045, Japan

Keywords: in silico Screening, Deep Learning, Compound-Protein Interaction
Understanding compound-protein interactions (CPI) is important for identifying hit compounds inthe early stages of drug discovery. Recent advances in the organization of bio-activity databases
and the development of deep learning (DL) methods have enabled rapid and accurate predictions of
CPIs, but DL methods still have limitations in explaining why the predictions were made [1,2]. In
this study, we propose a CPI prediction model that allows physical and biological interpretation forthe prediction results. In the first step, we constructed a multimodal model using Graph
Convolutional Networks (GCN) for compounds and Convolutional Neural Networks (CNN) for
proteins to predict CPI. In the second step, we used Integrated Gradients (IG) [3] to visualize thecontribution of the atoms of compounds and amino acid residues of proteins to the predicted
results, and discussed them. As a result of visualization, it is suggested that our model focuses, inthe process of prediction, on the characteristics of the interaction sites in the CPI. The
visualization methods in this study were shown to be useful for evaluating the validity of theprediction results in the DL-based CPI prediction model.
[1] Hamanaka, M.; Taneishi, K.; Iwata, H.; Ye, J.; Pei, J.; Hou, J.; Okuno, Y.; CGBVS-DNN:
Prediction of Compound-protein Interactions Based on Deep Learning, Mol. Inf., 2017, 36,
1600045.
[2] Verma, N.; Qu, X.; Trozzi, F.; Elsaied, M.; Karki, N.; Tao, Y.; Zoltowski, B.; Larson, E.C.;
Kraka, E. SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction, Int. J.
Mol. Sci., 2021, 22, 1392.
[3] Sundararajan, M., Taly, A., & Yan, Q. Axiomatic attribution for deep networks, International
conference on machine learning. PMLR, 2017, p. 3319-3328.

P08-12

Lead optimization through active learning using freeenergy perturbation

1

Kairi Furui 1

Masahito Ohue 1

furui@li.c.titech.ac.jp

ohue@c.titech.ac.jp

Department of Computer Science, School of Computing, Tokyo Institute of Technology, 4259 G3-56 Nagatsuta-cho, Midori-ku, Yokohama City, Kanagawa 226-8503, Japan

Keywords: Lead optimization, Free energy perturbation, Active learning
Exploring lead compounds with potent inhibition from hit compounds is one of the challenging
tasks in drug discovery. Free energy perturbation (FEP) calculations have attracted attention as alead optimization method that can predict binding affinities with extremely high accuracy [1].
However, FEP calculation requires huge computational time compared to other quantitative
structure-activity relationships (QSAR) and molecular docking. Therefore, it is essential to explore
derivatives in the possible chemical space more efficiently with fewer t rials to take advantage of
FEP for lead optimization.
Recently, active learning-based lead optimization based on relative binding free energy
calculations was recently proposed [2]. Still, the effectiveness of this approach in practice has notbeen comprehensively investigated. This study aims to establish a workflow for FEP calculations
based on active learning to explore new compounds with desirable binding free energies in fewer
trials.
[1] Wang, L.; et al., Accurate and reliable prediction of relative ligand binding potency inprospective drug discovery by way of a modern free -energy calculation protocol and force field,
Journal of the American Chemical Society, 2015, 137(7), 2695-2703.
[2] Guesev, F.; et al., Active learning guided drug design lead optimization based on relative binding
free energy modeling, ChemRxiv (preprint), 2022, 10.26434/chemrxiv-2022-krs1t.

P08-13

Prediction of Class A GPCR-Compound interactions by
deep learning focusing on ligand binding site protein
sequences
Haruki Yamane 1

yamane@cb.cs.titech.ac.jp

1

Takashi Ishida 1

ishida@c.titech.ac.jp

Tokyo Institute of Technology

Keywords: Machine Learning, Activity Prediction

G protein-coupled receptors (GPCRs) are classified into six classes, A-F, based on physiologicaland structural characteristics [1]. Class A GPCRs, which constitute the largest GPCR class, have aligand-binding site at the helix. The identification of the ligands for these GPCRs will lead to thediscovery of new drugs discovery and functional roles. In recent years, deep learning has been
widely used to predict protein-compound interactions, and many studies have used the full lengthof the protein sequence as input for various protein families [2-4]. However, if the helix region isfound to be important for compound binding, as in the case of Class A GPCRs, the terminal region
and intracellular and extracellular loop regions may be encoded as noise. In this study,
we created a compound interaction dataset focusing on Class A GPCRs and proposed a model for
predicting protein-compound interactions that focuses on the amino acid sequence of the helix
portion of Class A GPCRs.
[1] Secker A. Halling-Brown M. et al. Davies, M.N. GPCRtree: online hierarchical classification of
gpcr func- tion. BMC Res Notes, Vol. 1, p. 67, 2008.
[2] Hakime O ̈ z tu ̈ r k, Arzucan O ̈ z gu ̈ r, and Elif Ozkirimli. DeepDTA: deep drug–target binding
affinity prediction. Bioinformatics, Vol. 34, No. 17, pp. i821–i829, 09 2018.
[3] Masashi Tsubaki, Kentaro Tomii, and Jun Sese. Com- pound–protein interaction prediction with
end-to-end learning of neural networks for graphs and sequences. Bioinformatics, Vol. 35, No. 2,
pp. 309–318, 07 2018.
[4] Lifan Chen, Xiaoqin Tan, Dingyan Wang, Feisheng Zhong, Xiaohong Liu, Tianbiao Yang,
Xiaomin Luo, Kaixian Chen, Hualiang Jiang, and Mingyue Zheng. TransformerCPI: improving
compound–protein interac- Vol.2022-BIO-69 No.3 2022/3/10 tion prediction by sequence-based
deep learning with self-attention mechanism and label reversal experiments. Bioinformatics, Vol.
36, No. 16, pp. 4406–4414, 05 2020.

P08-14

AI-AAM. Lead-to-lead scaffold hopping
for drug repositioning
Jun Nakabayashi 1

Kyosuke Tsumura 1

Shino Ohira 1

jun.nakabayashi@fujifilm.com

kyosuke.tsumura@fujifilm.com

shino.ohira@fujifilm.com

Kaoru Iwato 1

Mao Tanabe 2

Yuko Usami 2kaoru.iwato@fujifilm.com

mtanabe@nibiohn.go.jp

y-usami@nibiohn.go.jp

Ryuichi Sakate 2

Tomonori Kimura 2

rsakate@nibiohn.go.jp
1

2

t-kimura@nibiohn.go.jp

Analysis Technology Center, FUJIFILM Corporation, 210 Nakanuma, Minamiashigara-shi,
Kanagawa 250-0193, Japan
National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8, Saito-Asagi, Ibaraki
City, Osaka 567-0085, Japan

Keywords: AI Drug Discovery, LBDD, Rare Disease, DDrare, AI-AAM
Drug development for rare and intractable diseases is challenging and drug repositioning (DR)
has attracted attention to reduce the time and cost of the process [1]. While DR based on biologicalnetworks became common recent years, we invented a new way for DR by focusing on the efficientlead-to-lead scaffold hopping using intermolecular interactions.
We previously reported AI-Amino Acid Mapping (AI-AAM), the in-silico hit-to-hit scaffold
hopping method in which the same order of the binding affinity as the original ligand wasmaintained when a new compound had the high AAM similarity to the active ligand [2]. Now, weapplied the method for lead-to-lead hopping for DR. Using DDrare [3], we selected SYK, a drug
target for IgA nephropathy, etc. Starting from a known SYK inhibitor candidate BIIB-057 [4], weaimed to discover other lead ligands from 44,503 compounds composed of biologically active
compounds including approved drugs and compounds in clinical phase. We found a known
anticancer drug candidate XC608 [5] with the highest AAM similarity score to BIIB-057 andconfirmed almost the same inhibition to SYK as the original ligand (Fig.). This implies that XC608
is a repositionable drug for IgA nephropathy, etc.
AAM

BIIB-057 [4] (IC50 = 3.9 nM)

XC608 [5] (IC50 = 3.3 nM)

Fig. Scaffold hopping from BIIB-057 (left) to XC608 (right) via AAM (center)[1] R. Sakate, T. Kimura. Drug repositioning trends in rare and intractable diseases. Drug discov.
today 2022, 7, 1789 .
[2] K. Tsumura.; et al., A newly developed method based on AI-oriented amino acid interaction
mapping (AI-AAM) for efficient virtual scaffold hopping, CBI Annual Meeting 2018, P2-02.
[3] DDrare: Database of Drug Development for Rare Diseases , https://ddrare.nibiohn.go.jp/[4] WO 2009/136995.
[5] WO 2011/035077.

P08-15

Understanding Molecular Mechanism ofDrug Off-Target Effect in Tyrosine-Protein Kinase LCK
Using Molecular Dynamics Simulation
Hidetoshi Tage 1
tage.hidetosi.86v@kyoto-u.ac.jp

Shigeyuki Matsumoto 1

Hiroaki Iwata 1

matsumoto.shigeyuki.4z@kyoto-u.ac.jp

iwata.hiroaki.3r@kyoto-u.ac.jp

Yuta Isaka 2

Biao Ma 2

Mitsugu Araki 1

isaka@riken.jp

biao-ma@riken.jp

araki.mitsugu.6w@kyoto-u.ac.jp

Atsushi Tokuhisa 2
tokuhisa@riken.jp

Ryo Kanada 2

Yasushi Okuno 1, 2

ryo.kanada@riken.jp

okuno.yasushi.4c@kyoto-u.ac.jp

1

Graduate School of Medicine and Faculty of Medicine, Kyoto University, 53 Kawahara-cho,
Shogoin Sakyo-ku, Kyoto, 606-8507, Japan
2
Riken Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, 6500047, Japan

Keywords: Molecular dynamics, Ligand docking, Adverse drug reaction, supercomputer “Fugaku”,
Off-target
An unexpected interaction of a drug with a protein other than its target, i.e., off-target interaction,
causes adverse drug reactions (ADRs), significantly hampering effective clinical treatment. Accordingly,
the off-target interactions with the ADRs-associated proteins are investigated in the early stage of drug
development. Nevertheless, the molecular mechanisms, including the binding sites and the binding
manners on the off-target protein, have not been intensively investigated, limiting the establishment of
the strategy to avoid the ADRs.
Here, using molecular dynamics (MD) simulation on the state-of-the-art supercomputer “Fugaku”
equipped with abundant nodes, we investigated the molecular mechanism of the off-target effectattributed to lymphocyte-specific protein tyrosine kinase (LCK), a well-known off-target protein
causing immunodeficiency. We selected 20 drugs, which are known to cause immunodeficiency or theother ADRs, and individually explored the binding events occurring on the overall molecular surface of
LCK using an MD simulation technique, ColDock [1]. The comprehensive analysis of the obtained MD
data identified several binding regions characteristic of the immunodeficiency-associated drugs. In thispresentation, we will discuss the molecular mechanism underlying the off-target effect by investigating
the relationship between their bound states and the functional deficiency found in LCK [2] from thepoint of view of dynamic behaviors.

[1] Takemura K.; Sato C.; Kitao A., ColDock: Concentrated Ligand Docking with All-Atom
Molecular Dynamics Simulation, J Phys Chem B, 2018, 122, 7191–7200.
[2] Hauck F.; Randriamampita C.; Martin E.; Gerart S.; Lambert N.; Lim A.; et al., Primary T-cell
immunodeficiency with immunodysregulation caused by autosomal recessive LCK deficiency, J
Allergy Clin Immunol, 2012, 130, 1144–1152.e11.

P08-16

Molecular Generation
for Protein-Protein Interaction Inhibitor Design
focusing on Physicochemical Properties
Yuki Kojima 1

Takatsugu Kosugi 1

kojima@li.c.titech.ac.jp

kosugi@li.c.titech.ac.jp

Masahito Ohue 1
ohue@c.titech.ac.jp

1

Tokyo Institute of Technology, G3 building 10F, 4259-G3-56 Nagatsuta-cho, Midori-ku,
Yokohama, Kanagawa 226-8503, Japan

Keywords: protein-protein interaction (PPI), molecular generation model, machine learning
Protein-protein interactions (PPIs) are essential targets in drug discovery because of theirassociation with various diseases. PPI-targeting modulators have very different physicochemical
properties from conventional small molecule oral drugs, such as the "Rule-of-Five" (RO5).
Therefore, it has been difficult to efficiently generate and design PPI inhibitors using conventional
methods, including molecular generation models. In this study, we propose a molecular ge neration
model based on deep reinforcement learning, specialized for generating PPI inhibitor candidates.
By improving the scoring function of the existing molecular generation model for small molecules,
we have made it possible to generate compounds that are likely to inhibit PPIs. For future use in abiochemical assay, we also try to build a virtual library consisting of generated compounds by the
proposed method. The compounds in this library are considered more suitable for recent PPI
inhibitor design than those in the existing PPI-oriented library.

P08-17

Multi-objective Bayesian optimization for antimicrobial
peptides design using non-natural amino acids

1

2

Yuki Murakami 1

Shoichi Ishida 1

W225431d@yokohama-cu.ac.jp

lshida.sho.nm@yokohama-cu.ac.jp

Yosuke Demizu 1,2

Kei Terayama 1

demizu@nihs.go.jp

terayama@yokohama-cu.ac.jp

Yokohama City Univ., 1-7-29 Suehirocho, Tsurumi-ku, Yokohama-shi, Kanagawa, 230-0045,
Japan
National Institute of Health Science, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki-shi,
Kanagawa, 210-9501, Japan

Keywords: Bayesian optimization, Gaussian regression, Machine learning, Antimicrobial peptides,
non-natural amino acids
Multidrug-resistant bacteria that have acquired resistance to many antibiotics have become a severe
problem. Antimicrobial peptides (AMPs) have attracted attention as a strategy against multidrugresistant bacteria because they act directly on cell membranes. On the other hand, AMPs screening andidentification by experimental techniques are laborious and time-consuming tasks since AMPs design
is often based on researchers' intuition and experience. To efficiently explore AMPs, several
studies have developed machine learning models to predict antimicrobial activities of AMPs fromsingle amino acid letter sequence information [1-3]. Recently, non-natural amino acids have been usedin AMPs design for metabolic and structural stability [4]. However, most of these prediction models
do not handle non-natural amino acids and efficient strategies for a design of experiments based onmachine learning models have not yet been developed to design AMPs with non-amino acids.
Here, we proposed a method using multi-objective Bayesian optimization [5] to search for the optimal
structure with a small number of experiments for AMP design by considering non-natural amino acids.
As the input feature of surrogate models for Bayesian optimization, we used Morgan and MACCS
fingerprints. The objective variables were toxicity to red blood cells and antibacterial activities against
six bacteria including multi-drug resistant bacteria. To solve the multi-objective optimization problem,
we calculated the probability of improvement (PI) for each objective and used the product of all thePIs scores as an acquisition function. We generated candidate AMPs with at least two mutations basedon the base sequence, calculated their scores exhaustively, and selected AMPs with these better scores.
Seven AMPs selected by this calculation were synthesized by the solid-phase synthesis method andevaluated by the minimum inhibitory concentration method for further optimization. At this conference,
we report both the proposed method and the experimental results.
[1] Alice Capecchi; et al., Machine learning designs non-hemolytic antimicrobial peptides, Chem. Sci.,
2021, 12, 9221-9232[2] Supatcha Lertampaiporn; et al., Ensemble-amppred: Robust amp prediction and recognition using
the ensemble learning method with a new hybrid feature for differentiating amps, Genes, 2021, 12, 117[3] Andrejs Tucs; et al., Generating ampicillin-level antimicrobial peptides with activity-aware
generative adversarial networks, ACS Omega, 2020, 5, 22847-22851.
[4] Motoharu Hirano; et al., Development of Antimicrobial Stapled Peptides Based on Magainin 2
Sequence, Molecules, 2021, 26, 444[5] Yuichi Motoyama; et al., Bayesian optimization package: PHYSBO, Comput. Phys. Commun., 2022,
278, 108405

P08-18

Automated design of novel drugs using
patent literature
András Strácz 1,2

Jenny Viklund 1

astracz@chemaxon.com

jviklund@chemaxon.com

1

ChemAxon Ltd. , Váci út 133., Budapest, 1138, Hungary

2

Patcore. Inc, Sapia Tower 26F, 1-7-12 Marunouchi, Chiyoda-ku, Tokyo, 100-0005, Japan

Keywords: Patent literature, MMP method, Designing novel drugs
Predictive models and data-driven generation of new compounds are key capabilities when designingand optimizing small molecule drugs. This presentation will show how knowledge from previous drug
discovery projects can be utilized in rational drug design via a generic Matched Molecular Pairs (MMP)
model [1] based on exemplified structures in SureChEMBL [2] (Figure 1). To put the model into context,
using Design Hub [3], an example scientific hypothesis is created, and new compounds will be
automatically generated using the MMP model and prioritized using a predicted parameter. We will also
show how to securely share selected compounds within your project with an external CRO for synthesis.

Figure 1: Examples of the most common small molecule drug transformations in SureCHEMBL- Poster presentation- Patcore. Inc, represents Chemaxon. This poster presentation will be read by the member of Patcore,Inc. at the CBI.
[1] Hussain and Rea. Journal of Chemical Information and Modeling, 2010, 50 (3), 339-348[2] Papadatos, George, et al. Nucleic acids research 44.D1 (2016): D1220-D1228. DOI:
10.1093/nar/gkv1253[3] https://chemaxon.com/products/design-hub

P08-19

Automated hetero shuffling combined with FMO
calculations
Sundaram Arulmozhiraja 1
arulmozhiraja.sb@om.asahi-kasei.co.jp

1

Kazufumi Ohkawa 1
okawa.kf@om.asahi-kasei.co.jp

Asahi Kasei Pharma, 632-1 Mifuku, Izunokuni, Shizuoka 410-2321, Japan

Keywords: Hetero-shuffling, Fragment molecular orbital calculation, Protein-ligand interactions
Fragment molecular orbital (FMO), an accurate quantum mechanics-based theory [1-3],
calculations play the leading role in estimating protein-ligand interactions, and the results derived
can be utilized to support structure-based drug design [4]. In fact, several studies concluded thatinteraction energies obtained using FMO calculations correlate with the efficacy of drugs [5]. Hetero
shuffling is an approach manipulating atoms of the scaffold to generate potential new molecules
and ultimately used them in drug designing to improve physiochemical and biological activities of
hit compounds. To assist and speed up drug discovery projects, we herein developed a code, toutilize both hetero shuffling and FMO calculations, to automatically generate hetero-shuffled newmolecules bound to the parent protein structure. This code then prepares FMO input files for all the
newly generated complex structures.
Overall, we develop a methodology for practical hetero-shuffle-based molecule generation
combined with FMO calculations.
[1] Kitaura, K.; Ikeo, E.; Asada, T.; Nakano, T.; Uebayashi, M. Fragment molecular orbital method:
an approximate computational method for large molecules, Chem. Phys. Lett., 1999, 313, 701 ‒706.
[2] Fedorov, DG.; Kitaura, K. Extending the power of quantum chemistry to large systems with thefragment orbital method, J. Phys. Chem. A, 2007, 111, 6904 ‒ 6914.
[3] Fedorov, DG.; Nagata, T.; Kitaura, K. Exploring chemistry with fragment molecular orbital
method, Phys. Chem. Chem. Phys., 2012, 14, 7562 ‒ 7577.
[4] For example, Monteleone, S.; et al., Hotspot identification and drug design of protein-protein
interaction modulators using fragment molecular orbital method, J. Chem. Inf. Model, 2022, 62,3784 ‒ 3799.
[5] For example, Arulmozhiraja, S.; et al., Comparative binding analysis of dipeptidyl peptidase IV(DPP-4) with antidiabetic drugs – An ab initio fragment molecular orbital study, PLoS ONE, 2016,
11, e0166275.

P08-20
In Silico Identification of Natural Product Compounds as a
Selective Endoplasmic Reticulum α-Glucosidase II Inhibitor
using Pharmacophore-based Virtual Screening and MolecularDocking Studies
Mochammad Arfin Fardiansyah Nasution 1,2mochammad.arfin@protein.osaka-u.ac.jp

Chioko Nagao 1,2c_nagao@protein.osaka-u.ac.jp

Kenji Mizuguchi 1,2*
kenji@protein.osaka-u.ac.jp
1

2

Laboratory for Computational Biology, Institute for Protein Research, Osaka University, 3-2
Yamadaoka, Suita, Osaka 565-0871, Japan
National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki,
Osaka, 567-0085, Japan

Endoplasmic reticulum (ER) α-glucosidase II (GluII) is a host-cell enzyme that is required byvarious enveloped viruses in the replication and morphogenesis processes of their life cycle 1 . The
selective inhibition of GluII enzyme, without affecting other glucosidases such as ER α-glucosidase
I (ER GluI) and lysosomal α-glucosidase (GAA), could lead to an effective alternative approach intreating various viral infections in the human body 2 . Natural products (NP) have been longconsidered as an invaluable source of drugs with diverse bioactivities, including antiviral activities 3 .
Herein, 1,073,049 NP compounds from ten databases were subjected into pharmacophore modellingand molecular docking studies to determine the most potent NP compound that can selectively
inhibit GluII enzyme. Pharmacophore models were created from the existing GluII crystal
structure 4,5 , followed by induced-fit based-docking studies. Additionally, a series of ADME-Tox
screening tests were performed to determine the pharmacological and pharmacokinetic propertiesof the selected NP compounds. From these studies, two NP ligands, namely SID461843168 and
UNPD170308 showed the highest binding affinities with the GluII enzyme, with a ΔG binding scoreof -11.17 kcal/mol and -11.03 kcal/mol, respectively. Furthermore, SID461843168 ligand formed
an H-bond interaction with both catalytic site residues Asp564 and Asp640, while UNPD170308
only formed an H-bond with Asp640. Additionally, these compounds demonstrate better affinities
with GluII than GAA and GluI, according to the induced-fit docking results. Further computational
analysis will be conducted on these compounds to examine the binding stability using molecular
dynamics simulations.
Keywords: Endoplasmic reticulum α-glucosidase II, antiviral, natural product compounds,
pharmacophore-based virtual screening, induced-fit docking.
Reference:[1]
Williams, S. J.; Goddard-Borger, E. D. α-Glucosidase Inhibitors as Host-Directed Antiviral
Agents with Potential for the Treatment of COVID-19. Biochem. Soc. Trans. 2020, 48 (3),Kiappes, J. L.; et al., ToP-DNJ, a Selective Inhibitor of Endoplasmic Reticulum α[2]Glucosidase II Exhibiting Antiflaviviral Activity. ACS Chem. Biol. 2018, 13 (1), 60–65.
Newman, D. J.; Cragg, G. M. Natural Products as Sources of New Drugs over the Nearly[3]
Four Decades from 01/1981 to 09/2019. J. Nat. Prod. 2020, 83 (3), 770–803.
[4]
Caputo, A. T.; et al., Structures of Mammalian ER α-Glucosidase II Capture the Binding
Modes of Broad-Spectrum Iminosugar Antivirals. Proc. Natl. Acad. Sci. U. S. A. 2016, 113(32), E4630–E4638.
[5]
Karade, S. S.; et al., N-Substituted Valiolamine Derivatives as Potent Inhibitors ofEndoplasmic Reticulum α-Glucosidases I and II with Antiviral Activity. J. Med. Chem. 2021.

P10-01
Gentamicin-induced hearing loss: a retrospective study
using the Food and Drug Administration Adverse Event
Reporting System and a drug−gene network analysis using
the DIseAse MOdule Detection algorithm
Mizuki Tanaka 1

Kiyoka Matsumoto 1

165050@gifu-pu.ac.jp

165079@gifu-pu.ac.jp

Riko Satake 1
175058@gifu-pu.ac.jp

Misaki Inoue 1
175016@gifu-pu.ac.jp

Takaaki Suzuki 1,2
t07tor5489@hi3.enjoy.ne.jp

1

2
3
4

Yu Yoshida 1
175127@gifu-pu.ac.jp

Shiori Hasegawa 1
135056@gifu-pu.ac.jp

Mari Iwata 1,3
m-iwata@kifune.info

Kazuhiro Iguchi 4

Mitsuhiro Nakamura 1

iguchi@gifu-pu.ac.jp

mnakamura@gifu-pu.ac.jp

Laboratory of Drug Informatics, Gifu Pharmaceutical University, 1-25-4, Daigaku-Nishi, Gifu,
501-1196, Japan
Gifu Prefectural Government, 2-1-1 Yabutaminami, Gifu, 500-8570, Japan
Kifune Pharmacy, 2-23-2 Hasuike, Yanaizu-cho, Gifu, 501-6103, Japan
Laboratory of Community Pharmacy, Gifu Pharmaceutical University, 1-25-4, Daigaku-Nishi,
Gifu, 501-1196, Japan

Keywords: hearing loss, gentamicin, drug−gene interactions, disease module detection algorithm,
Food and Drug Administration adverse event reporting system
Hearing loss has a significant effect on patients’ quality of life. The objectives of the study wereto evaluate the relationship between gentamicin (GEN) and hearing loss using the Food and Drug
Administration Adverse Event Reporting system (FAERS) database and elucidate the potential
toxicological mechanism of GEN-induced hearing loss through a drug–gene network analysis. We
extracted GEN-associated genes (seed genes) and analyzed drug−gene interactions using the DIseAse
MOdule Detection (DIAMOnD) algorithm. The FAERS database had 10 745 188 reports from January
2004 to June 2019. The number of reports for drug induced hearing loss was 40 073. The top three
adverse events reported were hypoacusis (16 557 cases), deafness (13 140 cases), and deafness
unilateral (3177 cases). The lower limits of the 95% confidence intervals of the reporting odd ratios for
streptomycin, tobramycin, GEN, kanamycin, neomycin, amikacin, and netilmicin were greater than one.
The DIAMOnD algorithm presents a systematic analysis of the connectivity patterns of diseaseproteins and can be used to determine the predictive topological property using a Python script. For
DIAMOnD analysis, we retrieved the human protein−protein interaction dataset from the BioGRID
database. To identify the GEN-associated module constructed by this algorithm, we used the humanprotein−protein interaction dataset retrieved from the BioGRID database and the seed genes identified
using PharmGKB and DGIdb. We identified 60 GEN-associated genes using the DIAMOnD algorithm.
Several GEN-associated genes in the DIAMOnD algorithm were highly enriched in “Ras signaling
pathway,” “focal adhesion,” “MAPK signaling pathway,” “regulation of actin cytoskeleton,” “oxidative
phosphorylation,” and “ECM-receptor interaction.” Our analysis demonstrated an association
between several AGs and hearing loss using the FAERS database. Drug−gene network analysis
demonstrated that GEN may be associated with oxidative phosphorylation-associated genes andintegrin genes, which may be associated with hearing loss.
[1] Tanaka M. et al. HELIYON, 2021.7, e07429, doi: 10.1016/j.heliyon.2021.e07429

P10-02

Comprehensive analysis of the drugs that may induce
severe cutaneous adverse reactions using the adverse drug
event report database
Risa Tonegawa 1

Mizuho Asada 1

m226235@std.my-pharm.ac.jp

mizuho@my-pharm.ac.jp

Yoshihiro Uesawa 1
uesawa@my-pharm.ac.jp

1

Department of Medical Molecular Informatics, Meiji Pharmaceutical University, 2-522-1
Noshio, Kiyose, Tokyo, 204-8588, Japan

Keywords: JADER, adverse effect, Severe cutaneous adverse reactions, Stevens–Johnson syndrome,
toxic epidermal necrolysis
Purpose: Severe cutaneous adverse reactions (SCAR) like Stevens–Johnson syndrome and toxic
epidermal necrolysis are grave conditions that often have prognoses. Such conditions arecharacterized by necrotic and epidermal lesions, and are frequently accompanied by high fever,mucous membrane rash, blistering, and epidermal exfoliation. Moreover, few studies of medications
known to induce SCAR exist. We analyzed medications associated with SCAR induction using theJapanese Adverse Drug Event Report (JADER) published by the Pharmaceuticals and Medical
Device Agency.
Method: We utilized 14 preferred terms meaning SCAR based on standardized MedDRA queries.
We examined the tables that included JADER drug information, adverse events, and patient
demographics collected from April 2004 to March 2022. Furthermore, reported instances of SCAR
onset were investigated for all listed drugs. Finally, the reporting odds ratios (ROR) and Fisher's
exact test P-values were determined for all the identified drugs.
Results: We identified 542 potential SCAR-inducing drugs (P-value <0.05 and ROR >1), 45 ofwhich demonstrated high statistical significance (P-value <10−50 and ROR >2). These included
medications for acid-related disorders in addition to various systemic anti-microbial, anti-epileptic,
anti-inflammatory, and anti-rheumatic medications.
Discussion: We developed a comprehensive list, including previously identified medications suchas carbamazepine and loxoprofen, which appear to be associated with SCAR development. Thesefindings may be helpful for the early detection and treatment of SCAR.

P10-03

Development of Prediction Model for Adverse Events byUsing Spontaneous Report Database and Chemical
Structures
Keiko Ogawa 1
ogawak23@fc.ritsumei.ac.jp

Ryosuke Kojima 2

Hiroaki Iwata 2
iwata.hiroaki.3r@kyoto-u.ac.jp

Yasushi Okuno 2

kojima.ryosuke.8e@kyoto-u.ac.jp okuno.yasushi.4c@kyoto-u.ac.jp

1
2

Rumiko Hosoki 1

r3hosoki@fc.ritsumei.ac.jp

Ritsumeikan Univ., 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan
Graduate School of Medicine, Kyoto Univ., 53 Kawahara -cho, Syogoin, Sakyo-ku, Kyoto, 6068507, Japan

Keywords: spontaneous report database, FAERS, adverse event, chemical structure, graphconvolutional networks (GCN)
The occurrence of adverse events caused by drugs can interrupt drug therapy. There is a need to theprevention of adverse events by expecting their occurrence at an early stage of drug development. To
date, various prediction models for specific adverse events have been de veloped from diverse
information such as chemical structures, aiming to predict the occurrence and the probable factors ofadverse reactions [1-2]. On the other hand, it is still unclear what kind of chemical structure of a drug
affects the occurrence of any adverse events.
Recently, an approach using graph convolutional networks (GCN) has been successfully applied tochemical information. GCN can represent the entire chemical structures of compounds by recognizing
compounds as graphs and convolving the information of not only neighboring atoms but also distantatoms. In this study, we tried to develop a multi -task model for predicting adverse events from thechemical structures of drugs using kGCN [3], a graph -based deep learning framework.
To construct the training dataset, we used the Czeek V dataset (obtained on March 2019) curated from
FDA Adverse Event Reporting System (FAERS), a spontaneous reporting database maintained by the
U.S. Food and Drug Administration (FDA). First, we perfor med data cleaning, which were removing
combination drugs and polymeric drugs, and eliminating inaccurate reports, then defined those with a
PRR ≥ 2.0 as having a risk of adverse event occurrence. As a result of data construction, we extracted
9,621,930 reports containing 2932 drugs and 14027 adverse events. From the extracted data, a multi task model was constructed by kGCN for 177 adverse events which are connected with 300 or moredrugs. In this presentation, the model construction, and the performance of constructed GCN model will
be explained together with which adverse events were predictable and which were difficult to predict
from the structural information. The influence of the chemical structures on the occurrence of adverse
events will be also discussed.
[1] Ambe, K.; et al., In Silico Approach to Predict Severe Cutaneous Adverse Reactions Using theJapanese Adverse Drug Event Report Database, Clinical and Translational Science, 2021, 756-763.
[2] Gadaleta, D., et al.; Prediction of the Neurotoxic Potential of Chemicals Based on Modelling ofMolecular Initiating Events Upstream of the Adverse Outcome Pathways of (Developmental)
Neurotoxicity, International Journal of Molecular Sciences, 2022, 23, 3053.
[3] Kojima, K.; et al., kGCN: a graph-based deep learning framework for chemical structures,
Journal of Cheminformatics, 2020, 12, 32.

P11-01

Analysis of Ion Transport Properties
in Artificial DNA Channels with Molecular Dynamics
Jun Takahashi 1,2

Akihiro Nakamura 1,2

jun.takahashi.r2@dc.tohoku.ac.jp

nakamura.akihiro.p1@dc.tohoku.ac.jp

Ibuki Kawamata 1
ibuki.kawamata@tohoku.ac.jp

1
2
3
4

Yusuke Sato 3
ysato@ics.kyutech.ac.jp

Takashi Tokumasu 2

Takuya Mabuchi 2,4

tokumasu@ifs.tohoku.ac.jp

mabushi@tohoku.ac.jp

Tohoku Univ., 2-1-1, Katahira, Aoba-ku, Sendai 980-8577, Japan
Institute of Fluid Science, Tohoku Univ., 2-1-1, Katahira, Aoba-ku, Sendai 980-8577, Japan
Kyushu Institute of Technology, 680-4 Kawazu, lizuka, 820-8502, Japan
Frontier Research Institute of Interdisciplinary Sciences, Tohoku Univ., 2-1-1, Katahira, Aobaku, Sendai 980-8577, Japan

Keywords: Molecular dynamics, DNA nanotechnology, Artificial molecular sensor
The transport of ions across lipid bilayers is done selectively by membrane protein channels, which
control various reactions in our body. The development of artificial ion channels by biomimicking
the characteristics of natural membrane protein channels lead s to their application as artificial nanobiological sensors. The DNA nanotechnology enables the creation of arbitrary three-dimensional
structures and multimolecular modification and facilitate s the fabrication and modification of
structures by the precise sequence designs at the single nucleotide level. Various artificial DNA
channels have been developed to achieve the selective transport of molecules with different particle
sizes 1 . However, the artificial DNA channels capable of more selective transport, such as transportof specific ions in specific proportions, have not been realized. Moreover, experimental methods
have found it difficult to discuss the details of ion transport phenomena at the nanoscale . Therefore,
the objective of this study is to elucidate the effects of the pore diameter and the modification ofinternal pores on ion transport properties using molecular dynamics simulations. For the models
with different pore sizes, three different DNA channel models (3-helix DNA channel 2 , 4-helix DNA
channel 3 , 6-helix DNA channel 4 ) corresponding to 3 nm, 8 nm, and 10 nm, are used. For themodification models, the ethyl group modification, which makes partially hydrophobic inside thepores, is employed in the present study.
[1] Thomsen, R. P.; Malle, M. G.; Okholm, A. H.; Krishnan, S.; Bohr, S. S. R.; Sørensen, R. S.;
Ries, O.; Vogel, S.; Simmel, F. C.; Hatzakis, N. S.; Kjems, J. A Large Size-Selective DNA
Nanopore with Sensing Applications. Nat. Commun., 2019, 10 (1).
[2] Park, S. H.; Barish, R.; Li, H.; Reif, J. H.; Finkelstein, G.; Yan, H.; LaBean , T. H. Three-Helix
Bundle DNA Tiles Self-Assemble into 2D Lattice or 1D Templates for Silver Nanowires. Nano
Lett., 2005, 5 (4), 693–696.
[3] Rangnekar, A.; Gothelf, K. V.; Labean, T. H. Design and Synthesis of DNA Four -Helix Bundles.
Nanotechnology, 2011, 22 (23).
[4] Yoo, J.; Aksimentiev, A. Molecular Dynamics of Membrane -Spanning DNA Channels: ConductanceMechanism, Electro-Osmotic Transport, and Mechanical Gating. J. Phys. Chem. Lett., 2015, 6 (23),4680–4687.

P11-02
Luminescent control of DNA-scaffolded BRET systemusing strand displacement reaction
Fumiaki Takano

Yuki Minamidek179263@kansai-u.ac.jpk119802@kansai-u.ac.jp

Tomoya Niki

Shiori Tabanak349342@kansai-u.ac.jpk053995@kansai-u.ac.jp

Akinori Kuzuya
kuzuya@kansai-u.ac.jp

Department of Chemistry and Material Engineering Kansai University, 3-2-35 Yamate-cho,Suita-Shi, Osaka, Japan

Keywords: DNA, Bioluminescence resonance energy transfer (BRET), Fluorescence resonance E
energy transfer (FRET)
Fluorescent dyes are used as functional sensors to visualize the physiological state of cells. In
microscopic observation methods that label with fluorescent dyes, there are problems such as fadingof the fluorescent dyes and cytotoxicity due to irradiation with strong excitation light. These
problems can be avoided by using luciferase as an excitation source to convert chemical energy into
light energy without the need for excitation light.
We therefore focused on luciferase called NanoLuc [1] . This is a modified version of luciferase fromdeep-sea shrimp, and NanoLuc, in which NanoLuc is split into two parts, is already available. It is
composed of a small fragment of 11 amino acids and a large fragment. Although they have noluminescence activity by themselves, they are reconstituted with high affinity to become NanoLuc andregain luminescent activity. However, the wavelength of luciferase is fixed depending on the substrate.
It usually a fusion protein of luciferase and fluorescent proteins through an appropriate linker is formed
to modulate the wavelength [2] . While this bioluminescence resonance energy transfer (BRET) systemallows efficient energy transfer, it requires optimization of the linker sequence and length through laborintensive genetic modification experiments.
In this study, we developed a BRET system that uses DNA as a scaffold between luciferase andfluorescent dyes. White light emission was also achieved by mixing RGB emission. This system allows
the user to select multi-colored luminescence by simply adjusting the concentrations of the three DNA
complexes. And the color tone changes were observed in real time by using hybridization and strand
displacement reactions.
[1] A. S. Dixon, et al., Anal. Chem. 2016, 11, 400-408[2] S. Sihuai, et al., Int. J. Mol. Sci, 2016, 17, 1704.

P11-03

Long-range and multi-step intramolecular energy transfer
by BRET/FRET system
Yuki Minamide

Fumiaki Takano

k119802@kansai-u.ac.jpk179263@kansai-u.ac.jp

Koichi Tanimoto

Akinori Kuzuya

k769965@keio.ac.jp

kuzuya@kansai-u.ac.jp

Department of Chemistry and Material Engineering Kansai University, 3–2–35 Yamate–cho,Suita–shi, Osaka, Japan

Keywords: DNA, Bioluminescence resonance energy transfer (BRET)
Plants have the complex between the photosynthetic reaction center and the light-harvesting antennacore proteins to use scarce solar energy efficiently. This complex has hundreds of chlorophyll. The
received light energy is passed from molecule to molecule to the reaction center by this complex.
This energy transfer is difficult to imitate. Because all fluorescent dye are excited at the same timewith bulk excitation source, we cannot observe energy transfer directly.
In this study, we focused on Bioluminescence Resonance Energy Transfer (BRET). BRET is aphenomenon in which the energy used by a luminescent substrate to emit light is transferred to afluorescent substance in the vicinity. It is necessary to link the luminescent and fluorescent proteins
with an appropriate linker to induce BRET [1]. This BRET system requires optimization of thelinker sequence and length through labor-intensive genetic modification experiments while it allows
efficient energy transfer. We have developed a DNA-linked BRET system by modifying one of thetwo complementary strands of DNA with NanoLuc luciferase [2], a luminescent protein, as a donor,
and a fluorescent dye as an acceptor on the other strand of DNA. In this system, color can be freelychanged by changing the type of fluorescent dye.
We attempted to directly observe energy transfer by modifying multiple fluorescent dyes within
the DNA molecule that serves as the linker. First, an azide was modified on the C-terminal side of
HiBiT, a split form of NanoLuc. The azide-modified HiBiT was conjugated with dibenzocyclooctyne(DBCO)-modified DNA via Cu-free click reaction. After hybridization with DNA inserted withfluorescent dye, NanoLuc was reconstituted and the emission spectrum was observed. As a result, it wasobserved that the greater the number of inserted fluorescent dyes, the more energy was transferred tothe opposite side of the NanoLuc-modified end.
[1] S. Sihuai, et al., Int. J. Mol. Sci, 2016, 17, 1704.
[2] A. S. Dixon, et al., Anal. Chem. 2016, 11, 400-408.

P12-01

Predicting Excipient Modulated Viscosity of Monoclonal
Antibody Formulations
Yoshirou Kimura 1
kimura.yoshirou@molsis.co.jp

1

MOLSIS Inc., 3-19-9, Hatchobori, Chuo-ku, Tokyo 104-0032, Japan

Keywords: Antibody medicine, Biologics, Molecular Property Prediction, Formulation, Excipient
When an antibody drug is administered by injection, a high-concentration solution of 100 mg/mL
or more is required because of the limited amount administered at one time . But at highconcentrations these formulations can cause an increase in viscosity. Subcutaneous injection
typically requires viscosities below 15–20 cP. Various excipient molecules, such as saccharides andamino acids, are used to adjust the viscosity. This experimental process is expensive and timeconsuming.
Therefore, we attempted to develop an in silico method to predict the tendency of viscosity in the
combination of antibodies and excipients in MOE [1]. First, we predicted the aggregation region of
the antibodies by various methods similar to SAP [2]. Next, docking simulation of the excipient was
performed over the entire regions of the antibody, and the region the the excipients bind to was
identified from the result. The degree of agreement between the two regions was used as an index
of viscosity. In this presentation, we will introduce the details of these methods and the results ofcomparison with experiments.
[1] Molecular Operating Environment (MOE), 2020.09; Chemical Computing Group ULC, 1010
Sherbrooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2020.
[2] Chennamsetty, N.; Voynov, V.; Kayser, V.; Helk, B.; Trout, B. L. Design of therapeutic proteins
with enhanced stability. Proceedings of the National Academy of Sciences , 2009, 106, 1193711942.

P12-02

Specific knockdown of KRAS mutant geneusing SNPD-siRNA repressed cell proliferationof pancreatic cancer cells in vitro and in vivo
Yoshiaki Kobayashi 1

Yoshimasa Asano 1

yoshiaki-kobayashi@g.ecc.u-tokyo.ac.jp

yoshimasa.asano@bs.s.u-tokyo.ac.jp

Atushi Sato 1
atsushisato0728@gmail.com

1

2

Hiroaki Taniguchi 2

Kumiko Ui-Tei 1

h-tani@keio.jp

ktei@bs.s.u-tokyo.ac.jp

Department of Biological Sciences, Graduate School of Science, the University of Tokyo,
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
Keio Cancer Center, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku,
Tokyo 160-8582, Japan

Keywords: SNPD-siRNA, KRAS mutation, pancreatic cancer
In RNA interference (RNAi), small interfering RNA (siRNA) functions to repress the expression of
its target mRNA with perfect sequence complementarity. Thus, siRNAs are a powerful tool not only
for functional genomics but also for therapeutic applications. To date, five siRNAs have been
approved as medicines, but all these siRNAs repress their target genes regardless of the mutations.
However, the nucleotide mutations in many oncogenes cause abnormal activation of cell growth.
Specific types of mutations observed in Kirsten rat sarcoma virus (KRAS) gene are known toassociate with tumor behavior such as accelerated tumor growth and metastasis in pancreatic orcolorectal cancer patients. Furthermore, KRAS-deficient mice are embryonic lethal. Therefore, it isnecessary to knock down the mutated KRAS gene alone without affecting the expression of its wildtype gene for therapeutic application of siRNA.
In this study, we developed a single nucleotide polymorphism-distinguishable siRNA (SNPDsiRNA) which specifically represses the mutated KRAS gene. Furthermore, we confirmed that it
repressed cell proliferation of pancreatic cancer-derived cell lines with the mutant KRAS gene invitro and in vivo. This is the first report of mismatch distinguishable siRNA and is expected to be
applicable for the treatment of undruggable diseases, including hereditary diseases.

P12-03

Development of Precise Genome Editing Technology by
Cell Cycle Depending Activation of CRISPR-Cas9
Daisuke Matsumoto 1 ,Kanae Kishi 1 ,Erina Matsugi 1 ,dmatsumo@hiroshima-u.ac.jp

d213023@hiroshima-u.ac.jp

d223872@hiroshima-u.ac.jp

Yuto Inoue 1 ,b193576@hiroshima-u.ac.jp
1

Kiyomi Nigorikawa 1 ,knigo@hiroshima-u.ac.jp

Wataru Nomura 1
wnomura@hiroshima-u.ac.jp

Hiroshima Univ.,1-2-3 Kasumi, Minami-ku, Hiroshima 734-8553, Japan

Keywords: Genome Editing, CRISPR-Cas9, Homology directed repair, Cell cycle
The CRISPR-Cas9 system was originally discovered as part of the bacterial immune systemagainst external DNA from organisms such as bacteriophages and plasmids [1]. It has become thepredilected simplified genome editing tool, because it is easier and less expensive to constructvarious target libraries compared to other editing technologies such as ZFN and TALEN. The system
has shown its efficient editing when used in human cells and model organisms. Although CRISPR
technology is the most useful method for genome editing, off-target effects that cause unexpected
mutations at pseudo-target DNA sequences could occur, similarly to those seen using as ZFN and
TALEN. Thus, in addition to endeavor to increase the efficiency of precise editing at on-targets,
off-target effects should be carefully addressed when genome editing tools are used, especially forclinical applications. Homology-directed Repair (HDR) is a DNA repair pathway based on template
DNA having homologous arm sequences adjacent to the cleavage site. In HDR events, repair oftarget sequences introduces precise sequence change. Therefore, increasing the ratio of DNA repair
through HDR over non-homologous end joining (NHEJ) is important for precise genome editing.
Recently, anti-CRISPR (Acr) protein inhibitors of the CRISPR-Cas9 system have been found[2]. The inhibitors, including AcrIIA4, were derived from bacteriophages targeting pathogenic
bacterial strains. AcrIIA4 from Listeria monocytogenes prophage binds strongly to SpyCas9-sgRNAcomplexes, but the binding affinity to ApoSpyCas9 is lower. It has been reported that AcrIIA4
efficiently inhibits SpyCas9 activity in mammalian cells. Furthermore, the inhibition of SpyCas9
activity by AcrIIA4 reduces off-target editing [3]. Thus, when anti-CRISPR expression can becontrolled by cell cycle, the activity of Cas9 endonuclease could also be controlled in the cells.
In this research, we fused the anti-CRISPR AcrIIA4 with the N-terminal region of humanchromatin licensing and DNA replication factor 1 (hCdt1) for activation in the S/G2 phases andinactivation in the G1 phase. hCdt1 is degraded by ubiquitin-mediated proteolysis through the
SCF Skp2 complex in the S/G2 phases. The cell cycle dependent Cas9 activation system was validated
using SpyCas9 endonuclease and AcrIIA4 in the cells. As expected, the system displayedautonomous Cas9 activity switch dependent on the cell cycle and increased the preciseness of target
gene editing [4].
[1] Barrangou, R.; et al., CRISPR Provides Acquired Resistance Against Viruses in Prokaryotes, Science,
2007, 315, 1709-1712.
[2] Rauch, B. J.; et al., Inhibition of CRISPR-Cas9 with bacteriophage proteins, Cell, 2017, 168, 150–
158.
[3] Shin, J.; et al., Disabling Cas9 by an anti-CRISPR DNA mimic, Sci. Adv., 2017, 3, e1701620.
[4] Matsumoto D. ; et al., A cell cycle-dependent CRISPR-Cas9 activation system based on an antiCRISPR protein shows improved genome editing accuracy, Commun. Biol., 2020, 3, 601.