March 7th
Monday,2022
OnlineABSTRACTS

Artificial force induced reaction method: A computational approach for exploringchemical reactions based on quantum chemical calculations
Satoshi Maeda
Professor
Affiliation:e-mail:Specialty:

ICReDD and Department of Chemistry,
Faculty of Science, Hokkaido University
smaeda@eis.hokudai.ac.jp
Theoretical Chemistry

ABSTRACTThe motion of atoms during a chemical reaction, called reaction path hereafter, can inprinciple be elucidated by repeatedly performing quantum chemical calculations at allenergetically feasible atomic configurations. However, the number of possible arrangements
involved in a reaction path can be huge. Previous studies have thus relied on assumptions, i.e.,
human inputs, concerning the atomic configurations along the targeted reaction path.
The human inputs may bias the results. To avoid that, we have developed an automatedreaction path search method called artificial force induced reaction (AFIR) [1]. AFIR explores
possible reaction paths automatically by inducing geometrical transformations in a molecule
systematically using a virtual force. Combining it with a chemical kinetics method called rate
constant matrix contraction (RCMC), on-the-fly kinetics simulation can be performed (FIG. 1).

FIG 1. A schematic illustrating on-the-fly kinetics simulation by AFIR and RCMC. Based on the
input, i.e., reactants, reaction temperature, reaction time, and computational level, AFIR finds
many reaction paths. RCMC identifies the most feasible reaction path from the resultant network.
Recently, we proposed a concept, quantum chemistry-aided retrosynthetic analysis(QCaRA). QCaRA predicts reaction paths affording a given product by an inverse reaction pathsearch from the product toward various reactant candidates using AFIR. In a proof-of-concept
study, we set difluoroglycine as the synthetic target. Then, a new synthetic route of producing a
difluoroglycine derivative was proposed as the reverse process of one of the obtained reaction
paths [2]. Further generalization of the algorithm to perform QCaRA to predict multistep reactions[3] and a new design strategy of transition metal catalysis [4] will also be presented.
REF.
1. Maeda, S.; Harabuchi, Y. WIREs Comput. Mol. Sci. 2021, 11, e1538.
2. Mita, T.; Harabuchi, Y.; Maeda, S. Chem. Sci. 2020, 11, 7569.
3. Sumiya, Y.; Harabuchi, Y.; Nagata, Y.; Maeda, S. Submitted (Preprint is available in ChemRxiv).
4. Matsuoka, W.; Harabuchi, Y.; Maeda, S. Submitted (Preprint is available in ChemRxiv).

Machine Learning for Heterogeneous Catalyst Design and Discovery
Takashi Toyao
Assistant Professor
Affiliation: Institute for Catalysis, Hokkaido University
e-mail: toyao@cat.hokudai.ac.jp
Specialty: Heterogeneous catalysis
ABSTRACT
Molecular/materials informatics has become a central paradigm in molecular andmaterials science thanks to the enormous potential it holds to revolutionize the design offunctional molecules/materials. However, although we have already seen proof-of-concept
examples that artificial intelligence (AI) can reduce the time and costs involved and also can findnew compounds, most of them have only been tested on benchmark problems and no
fundamentally-new molecules/materials or synthetic-transformations have been found. This is
primarily due to lack of data and that machine learning (ML), the main player in this campaign,is highly focused on optimization rather than finding novel compounds and phenomena(extrapolation).
Establishing “Catalysis Informatics” is even more challenging.[1] Although it is highly
related to materials informatics and chemoinformatics, it is distinguished by the fact that catalysis
is a time-dependent dynamic event controlled by the structures and chemical nature ofcatalytically active sites. In particular, heterogeneous catalysis is still a largely empirical science
due to the complexity of the surface chemistry involved. This situation causes lack of data as thecomputational costs to obtain accurate theoretical models for such complex heterogeneous
catalysis are currently prohibitively high and high-throughput experimental methods, which havebeen applied successfully to relevant fields, have not been explored fully at the current time. In
this regard, building ML models that effectively find novel catalysts within diverse chemical
space from “real world” experimental catalysis data (not from well-behaved computational data)is highly desirable.
In this context, our group has made a new ML approach which uses elemental features
as the input representations rather than inputting the catalyst compositions directly.[2,3] Namely,
in our proposed method, the elemental composition ratios are multiplied by elemental descriptors
such as electronegativities, melting points, atomic radii, etc. which are unique for each element.
For demonstration of our ML approach, literature data based on the oxidative coupling of methane(OCM) and water gas shift (WGS) reactions have been analyzed. Our ML approach, which
considers elemental features as input representations rather than the catalyst compositions, wassuccessfully applied, and new promising catalyst candidates for future research were proposed.
Latest results on CO2 hydrogenation will also be shown in the presentation.
REF.
[1]
T. Toyao, Z. Maeno, S. Takakusagi, T. Kamachi, I. Takigawa, K. Shimizu, Machine Learning
for Catalysis Informatics: Recent Applications and Prospects, ACS Catal. 10 (2020) 2260–
2297.
[2]
K. Suzuki, T. Toyao, Z. Maeno, S. Takakusagi, K. Shimizu, I. Takigawa, Statistical Analysis
and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse
Published Data, ChemCatChem. 11 (2019) 4537–4547.
[3]
S. Mine, M. Takao, T. Yamaguchi, T. Toyao, Z. Maeno, S.M.A. Hakim Siddiki, S. Takakusagi,
K. Shimizu, I. Takigawa, Analysis of Updated Literature Data up to 2019 on the Oxidative
Coupling of Methane Using an Extrapolative Machine-Learning Method to Identify Novel
Catalysts, ChemCatChem. 13 (2021) 3636–3655.

Modularly Designed Supramolecular Catalytic Assembly for the Strategic
Enantioselective Borylation of Remote C–H Bonds
Ronald Lazo Reyes
Specially Appointed Assistant Professor
Affiliation: ICReDD and Faculty of Science, Hokkaido University
e-mail: rlreyes@icredd.hokudai.ac.jp
Specialty: Organometallic Chemistry, Organic Synthesis
ABSTRACT
There exists in nature efficient catalytic systems that enable the challenging
functionalization of ubiquitous C–H bonds in diverse substrate classes. Enzymatic assemblies
have inspired the creation of wide-ranging synthetic supramolecular catalysts that mimic theirproficient activities. Despite the conceptual advances of these synthetic biomimetic systems, theelaborate preparations and limited substrate compatibility limit their utilities in organic synthesis.
With our interest in the development of novel methodologies for the transformation ofC–H bonds in readily available feedstock chemicals, we previously demonstrated the competentactivation and subsequent asymmetric borylation of C(sp3)–H bonds under Ir or Rh catalysts
utilizing a chiral monophosphite ligand system allowing the preparation of biologically-active
and synthetically useful organoboronates.1,2 Our collaboration with ICReDD scientists has given
us infinite opportunity to further extend the reactivity of our catalytic system. Thus, guided by
calculation derived transition-state models, we recently succeeded in the asymmetric borylationof remote -C–H bonds in aliphatic carboxylic amides and esters using a chiral, modular Irmonophosphite catalyst and a urea receptor ligand that recruits the substrate via noncovalentinteractions – a direct functionalization realized using a modularly designed supramolecularcatalytic assembly.3 In this aspect, the overall efficiency relies on the crucial interactions between
the catalytic system and the substrate within a reaction cavity or pocket that resembles the active
site of natural enzymes. Inspired and goal-driven by these results, further collaboration and joint
efforts has led us to the possibility of designing catalytic assemblies to target more distal C–H
bonds in the aliphatic hydrocarbon chain of common and readily available chemical feedstocks
including biomass resources allowing for a more sustainable and efficient organic synthesis.
In this presentation, the development of catalytic enantioselective borylation of C–H
bonds will be described in the context of reaction discovery, design, and utility. Strategic
convergence of experimental and theoretical studies will be outlined to emphasize our approachin targeting remote C–H bonds beyond the -position, further highlighting the need for thecreation of methodologies that will allow challenging transformations.

REFERENCES
1. R. L. Reyes, T. Iwai, S. Maeda, M. Sawamura, Iridium-catalyzed Asymmetric Borylation of
Unactivated Methylene C(sp3)–H Bonds. J. Am. Chem. Soc. 2019, 141, 6817–6821.
2. R. L. Reyes, M. Sato, T. Iwai, M. Sawamura, Asymmetric Synthesis of α-Aminoboronates via

Rhodium-Catalyzed Enantioselective C(sp3)–H Borylation. J. Am. Chem. Soc. 2020, 142,
589–597.
3. R. L. Reyes, M. Sato, T. Iwai, K. Suzuki, S. Maeda, M. Sawamura, Asymmetric Remote C–H
Borylation of Aliphatic Amides and Esters with a Modular Iridium Catalyst. Science 2020,
369, 970–974.

Novel quantum chemical approach to chemical reactions: reduced-dimensionality
reaction space and natural reaction orbital
Tetsuya Taketsugu
Professor
Affiliation: ICReDD and Department of Chemistry, Faculty of Science,
Hokkaido University
e-mail: take@sci.hokudai.ac.jp
Specialty: Theoretical and Computational Chemistry
ABSTRACTICReDD promotes fusion research to accelerate the development of useful chemical
reactions through "Chemical Reaction Design and Discovery (CReDD)" by integrating
computational science, information science, and experimental science. As a quantum chemical
computation team, our group is working on elucidating the mechanism of complex chemical
reactions in collaboration with experimental and information science groups by applying theartificial force induced reaction (AFIR) method, an automated reaction path search method, which
is a fundamental technology of ICReDD. We are also developing new methodologies for chemical
reaction analysis based on quantum chemistry. In this talk, I will introduce the reaction space
projector (ReSPer) method [1] and natural reaction orbital (NRO) concept [2] as newly developed
methods for chemical reaction analysis in our group.
In the quantum chemical approach, the mechanism of a chemical reaction is investigatedbased on the intrinsic reaction coordinate (IRC) defined on the potential energy surface (PES).
The AFIR method allows us to construct a global reaction route map containing multiple IRCs,
while the on-the-fly molecular dynamics method, which is based on electronic structure
calculations to determine the forces acting on atoms, generate classical trajectories withunrestricted motion on the full-dimensional PES. We have developed a method to visualize thereaction route map in a low-dimensional space by introducing inter-structure distances forstructure pairs, and named it ReSPer. It is also possible to project dynamical trajectories onto thereaction space defined based on the reaction route map, which is expected to serve as a general
method for analyzing chemical reaction mechanisms and dynamics.
Electronic degrees of freedom dominate the potential energy surface that determines
reaction paths and dynamical trajectories, and it is no exaggeration to say that the elucidation ofchemical reaction mechanisms is only complete when we understand the movement of electrons
associated with changes in molecular structure. The NRO method elucidates the electron motion
associated with the movement of nuclear coordinates by pairs of occupied and virtual orbitals,
and can extract regions along the reaction path where the electron motion is more pronounced. In
this talk, I will demonstrate the usefulness of NRO analysis by applying it to several reactions.
References
1. T. Tsutsumi, Y. Ono, and T. Taketsugu, Visualization of reaction route map and dynamical
trajectory in reduced dimension, ChemComm (Feature Article), 57, 11734-11750 (2021).
2. S. Ebisawa, M. Hasebe, T. Tsutsumi, T. Tsuneda, and T. Taketsugu, Natural reaction orbitals
for characterizing electron transfer responsive to nuclear coordinate displacement, Phys. Chem.
Chem. Phys., in press.

Negative Charge delivery in ice at 10 K: the role of surface OH radicals
Naoki Watanabe
Professor
Affiliation: Institute of Low Temperature Science, Hokkaido University
e-mail: watanabe@lowtem.hokudai.ac.jp
Specialty: Astrophysical chemistry, Atomic and Molecular Physics, Surface
chemistry
ABSTRACT
Since the study by von Grotthuss at the beginning of the 1800s, ice has been widely
accepted to carry a positive current by the transfer of excess protons via H3O+, similar to a “ptype” semiconductor. Although the proton transfer is still a matter of controversy at low
temperatures and is therefore addressed both experimentally and theoretically, this phenomenon
can be well described by the Grotthuss mechanism. In contrast, although “proton-hole transfer(PHT)” has been theoretically proposed as a possible mechanism for delivering negative charges
in water and ice, no clear evidence for the occurrence of this mechanism has been provided.
Consequently, the concept of negative current conductivity by the PHT has remained to be
confirmed experimentally for a hundred years.
Recently, we observed experimentally a negative constant current through ice at 10 K
when OH radicals coexist with electrons on the ice surface [1]. From quantum chemical
calculations and the experiments using the combination of photostimulated desorption andresonance-enhanced multi photoionization methods [2], the surface OH radicals were monitoredwith and without electrons [3], we conclude that once OH adsorbate captures an electron on thesurface, the surface OH- anions trigger the flow of negative current in ice by the sequential PHT
in ice. The OH- on the surface reproduces H2O after proton abstraction from neighbouring H2O.
Negative current conductivity was found to be preserved even at temperatures below 50 K, wherea positive current by the well-known Grotthuss mechanism is highly suppressed. The negative
charge delivery by the PHT was also confirmed in other hydrogen-bonding systems like SH2 andNH3 solids.
REF.
1. N. Watanabe, W. M. C. Sameera, H. Hidaka, A. Miyazaki, A. Kouchi, Ultraviolet-photon
exposure stimulates negative current conductivity in amorphous ice below 50 K, Chem. Phys.
Lett. 737, 136820 (2019)
2. A. Miyazaki, N. Watanabe, W. M. C. Sameera, Y. Nakai, M. Tsuge, T. Hama, H. Hidaka, A.
Kouchi, Photostimulated desorption of OH radicals from amorphous solid water: Evidence forthe interaction of visible light with an OH-ice complex, Phys. Rev. A. 102, 052882 (2020)
3. K. Kitajima, Y. Nakai, W. M. C. Sameera, M. Tsuge, A. Miyazaki, H. Hidaka, A. Kouchi, N.
Watanabe, Delivery of Electrons by Proton-Hole Transfer in Ice at 10 K: Role of Surface OH
Radicals, J. Phys. Chem. Lett. 12, 704 (2021)

Transient Charging and Charge Carrier Trapping in Semiconductor Nanocrystals
Vasudevan Pillai Biju
Professor
Affiliation: Research Institute for Electronic Science (RIES)
Hokkaido University
Email: biju@es.hokudai.ac.jp
Specialty: Nanomaterials, Single-molecules, Photophysics, Photochemistry
ABSTRACTThe size- and composition-dependent tunable bandgap or emission color of cadmium
chalcogenide quantum dots and hybrid halide perovskite nanocrystals are fascinating inventionsin nanoscience and nanotechnology.1,2 The tunable optical and electronic properties accompanied
these tiny crystals to various disciplines of basic research,1,3 making them promising for brilliantly
luminescent displays, high-efficiency photovoltaics, and sophisticated quantum computers.
However, photo-generated carriers in these tiny crystals show stochastically fluctuating
carrier/exciton recombination rates, rendering these semiconductors intermittent or blinking lightemitters at the single particle levels.4-9
Conversely, interparticle states and carrier traps enable delayed emission in closely packed
states, extending the photoluminescence lifetime to the microsecond scale. For single quantum
dots, the blinking time fluctuates on a wide time scale – from microseconds to minutes, preventing
the applications of these nanomaterials to on-demand light sources. The size, ligands, surface
morphologies, chemical compositions, the nature and density of defects, the intensity and energy
of incident light, and band-edge states affect the exciton, carrier, and intermittency
stabilities/durations. Although the relations of blinking to the intensity and energy of incident
light and the nature and density of defects are widely appreciated since 1995,3 the developmentof non-blinking quantum dots or nanocrystals is challenging even today.
This presentation highlights synthesis, quantum confinement, bandgap engineering, andoptical properties of selected semiconductor quantum dots with special references to the origins
of long photoluminescence lifetimes,10-12 and intermittent emission5-8,13 and suggests methods tocosset the blinking by carrier de-trapping, electron transfer, or defect passivation.
REF.
1. V. Biju, Chem. Soc. Rev. 43, 744 (2014).
2. L. Chouhan, V. Biju et al. Chem. Soc. Rev. 49, 2869 (2020).
3. V. Biju, et al. Chem. Soc. Rev. 39, 3031 (2010).
4. M. Nirmal, B. O. Dabbousi, M. G. Bawendi, et al. Nature 383, 802 (1995).
5. L. Chouhan, S. Ghimire, V. Biju, Angew. Chem. Int. Ed. 58, 4875 (2019).
6. E. M. Thomas, S. Ghimire, R. Kohara, V. Biju, et al. ACS Nano 12, 9060 (2018).
7. M. Hamada, S. Nakanishi, T. Itoh, M. Ishikawa, V. Biju, ACS Nano 4, 4445 (2010).
8. L. Chouhan, S. Ito, E. M. Thomas, V. Biju, et al. ACS Nano 15, 2831 (2021).
9. S. Yamashita, M. Hamada, V. Biju, et al. Angew. Chem. Int. Ed. 54, 3892 (2015).
10. S. Ghimire, V. Biju, et al. ACS Energy Lett. 4, 133 (2019).
11. V. C. Nair, C. Muthu A. L. Rogach, V. Biju, et al. Angew. Chem. Int. Ed. 56, 1234 (2017).
12. Z. Zhang, V. Biju, et al. ACS Nano, 16, doi.org/10.1021/acsnano.1c04944 (2022).
13. A. Dey, J. Ye, A. De, E. Debroye, V. Biju, et al. ACS Nano, 15, 10775 (2021).

Acceleration of Designing Chemical Reactions by Contextual Multi-armed Bandit
Algorithm
Tamiki Komatsuzaki
Professor
Affiliation: ICReDD and Research Center of Mathematics for Social
Creativity, Research Institute for Electronic Science (RIES), Hokkaido
University
e-mail: tamiki@es.hokudai.ac.jp
Specialty: Mathematical and Information Science, Chemical and Biological
Physics
ABSTRACT
Most typical machine learning approaches for chemical reactions is perhaps first to map
the desired quantities such as reaction yield, enantioselectivity etc measured for many systems
with different catalysts, substituents, and experimental conditions for a reaction to be explored toa certain feature space, and then to infer which feature(s) are most relevant to the desiredquantities, which may help to design a desired reaction system with high yield and/or highenantioselectivity. However, this approach requires a huge set of reaction data a priori, which
includes lots of undesired reactions of low performance to reveal the important features.
Reinforcement learning, utilized in the Monte Carlo tree search in alpha-GO, does notnecessarily require any set of data a priori, and is rather aimed at providing an experimentation
protocol on how to choose next experiment (that may have different catalyst, different substituentsand forth) in order to discover the desired reaction system(s) with as fewer experiments as possible,
with guaranteeing the accuracy of the prediction.
In this talk, we present our recent study on designing chemical reactions in which
contextual multi-arm Bandits algorithm1,2 in reinforcement learning is introduced to choose acatalyst to yield high enantioselectivity for an asymmetric hydroalkoxylation reaction. Here, as a
proof-of-concept of our algorithm, we used 38 reaction data associated with enantioselectivity
and more than 500 dimensional molecular features extracted by ISIDA descriptor, with theknowledge of the most desired reaction3. We show how one can accelerate to discover the mostdesired reaction before being performed all experiments.
This is a joint collaboration with List group (Dr. Tsuji) and Varnek group (Dr. Sidorov)in ICReDD.
REF.
1. K. Tabata, A. Nakamura, J. Honda, T. Komatsuzaki, A bad arm existence checking problem:
How to utilize asymmetric problem structure? Machine Learning, 109, 327-372 (2020)
2. K. Tabata, A. Nakamura, T. Komatsuzaki, Classification Bandits: Classification Using
Expected Rewards as Imperfect Discriminators, Pacific-Asia Conference on Knowledge
Discovery and Data Mining, 57-69 (2021)
3. N. Tsuji, P. Sidorov, Y. Nagata, A. Varnek, B. List, manuscript in preparation.

Approaches to Construction of Chemical Reaction Image Diagnosis System Using
Machine Learning
Yuki Ide
Specially Appointed Assistant Professor
Affiliation: Institute for Chemical Reaction Design and Discovery(ICReDD),
Hokkaido University
e-mail: ide.yuki@icredd.hokudai.ac.jp
Specialty: Structural Organic Chemistry and Material Chemistry
ABSTRACT
Appearance information such as color and shape are usually used to distinguishcompounds. When confirming the progress of chemical reactions, there are many opportunities
to judge the appearance of compounds. However, precision in judging the reaction yield varies
significantly depending on experimental skills and knowledge level. By making machine learningmodels1 using well-defined images of compounds, we expected that anyone can easily performthe diagnosis of reaction yields with high reproducibility, instead of using unclear parameters
based on empirical results.
In this presentation, I would like to talk about the construction of an imaging diagnosis
system using machine learning models, collaborating with Prof. Takikawa's group (ICReDD). In
order to conduct image diagnosis of reaction yields, it is necessary to discriminate quantitatively
compound ratios before and after the reaction. The effective collection of images in appropriate
formats for machine learning is also an important factor. We proposed to use commerciallyavailable sugar (sucrose) and table salt (NaCl: sodium chloride) to create a large number ofdatasets with desired mixing ratios (wt%) (Fig. 1a). To ensure higher diagnostic accuracy, we
attempted to normalize the imaging capture settings. After optimization of the amount and shapeof the mixture samples, image diagnosis systems embedded with the pre-trained model (using 500
images) were constructed. A third person uploads an image prepared with any mixing ratio to thediagnostic imaging system, and the predicted values are displayed on the system within a few
minutes (Fig. 1b). It is also available to provide observed-predicted plots, MAE, RMSE, and R2
values as evaluation indicators (Fig. 1c). Furthermore, we can predict with high accuracy the
mixture sample ratio of α-glycine and γ-glycine, which have different crystal polymorphs2.

Fig. 1 Quantitative image diagnostic system using sugar and salt. (a) Commercial sugar and salt. (b) Simple
image diagnosis system. (c) Image diagnosis system capable of exporting accuracy evaluation indicators.
1. Y. LeCun, Y. Bengio, G. Hinton, Nature, 2015, 521, 436–444.
2. G. Han, S. Thirunahari, P. S. Chow, R. B. H. Tan, CrystEngComm, 2013, 15, 1218–1224.

Data Representations for Deep Learning and Chemistry-Related Applications
Sheng Hu
Specially Appointed Assistant Professor
Affiliation: ICReDD, Hokkaido University
e-mail: hu.sheng@icredd.hokudai.ac.jp
Specialty: Deep Learning on Graphs, Data Wrangling, Information Retrieval
ABSTRACTThe ultimate goal of modern drug discovery is to find the target molecules with desired
chemical properties, while the potential chemical space of drug-like compounds is 1023−1060.
Visual graph query composition can assist modern drug discovery. Instead of
exploratory searching given a subgraph query in graph databases and showing matched graphs,
we prefer to use generative models to grow novel molecules on the given subgraph. In theapplications of drug discovery, such a subgraph query is usually called a scaffold (i.e., privilegedor bioactive scaffold), and performs as a core structure in the molecule to preserve the preferable
bioactivity properties. The generated novel molecular graphs are supergraphs of the scaffold thus
being guaranteed to contain the scaffold to reveal the chemical properties. Fixing the scaffold
usually dramatically reduces the search space of the desired drug thus saving experts’ time andcost.
Due to surprising success of deep neural network (DNN) models these days, twocategories of representations used in DNN-based models emerge in the drug discovery domain.
(1) simplified molecular input line entry system (SMILES) strings representation. Several earlyworks were proposed to learn the SMILES grammar using RNN architectures and then generatecorresponding SMILES strings from the trained models. These methods have limitations tolearning the unrelated grammar and thus have low chemical validity from generated SMILES. A
recent trend is (2) undirected labeled graph representation. It is more natural to learn the originalgraph structure by using graph neural networks (GNNs). This representation can easily achieve
higher chemical validity. In our work, we adopt the graph-based representation along with GNN
models as our generative models. GNN models are employed during the visual graph querycomposition process to generate completed candidates.
In this talk, we present our recent study1 on a GNN-based molecular graph generation
system, to allow users to edit the intermediate graph candidates during the molecule design
process in multiple steps, utilizing the edit operation to predict the user’s real intention to improve
effectiveness. We design an interactive substructure-by-substructure adopt process to verify thisidea. This process guarantees the involvement of user decisions to interact with a generative usercentered AI system, which differentiates our work from previous studies that generate graphs ina single run. We test our system using a real-world molecular dataset containing nearly one
million graphs to show the performance.
REF.
1. S. Hu, I. Takigawa, C. Xiao, Edit-Aware Generative Molecular Graph Autocompletion for
Scaffold Input, The 36th AAAI Conference on Artificial Intelligence (AAAI) Workshop on
Deep Learning on Graphs (DLG) (2022)

Mechanochemical Organic Synthesis: A Revolution Platform
Hajime Ito
Professor
Affiliation: ICReDD and Research Center of Mathematics for Social
Creativity, Graduate School of Engineering, Hokkaido University
e-mail: hajito@eng.hokudai.ac.jp
Specialty: Organic Chemistry, Mechanochemistry, Coordination Chemistry,
Organic Materials
ABSTRACT
Mechanochemical reactions have recently attracted attention as a new platform for thedevelopment of environmentally friendly organic reactions, as they can significantly reduce theusage of organic solvents and reduce the reaction times. Furthermore, by exploiting the uniqueenvironment of mechanochemical reactions, new reactions that cannot be found in solvents are
expected to develop. It is expected that many organic chemists will make use of mechanochemical
reactions in the future. The limited number of reactions tend to be used more frequently thanothers in the synthesis of organic compounds. It thus would be useful to focus on the most widelyused reactions. The Suzuki-Miyaura and Hartwig-Buchwald couplings are extremely important
reactions, representing 32% of those used in drug discovery. However, under mechanochemical
conditions, catalyst deactivation and poor reactivity with solid substrates has been a problem. We
found that the addition of 1,5-COD and heating conditions dramatically improve the efficiencyof these cross-coupling reactions of solid substrates.1-4 Grignard reaction is one of the fundamental
and most widely used organic reactions developed by Victor Grignard 120 years ago. To conduct
this reaction mechanochemically, we thoroughly investigated the reaction conditions andachieved almost the same reactivity as the Grignard reagent synthesized in solution under ballmill conditions.5 We also developed mechano-redox reactions that mimics the photoredox
reaction, which has attracted much attention recently, using piezoelectric materials.6,7
Mechanochemical polymer functionalization will also be presented.8
REF.
1. Kubota, K.; Seo, T.; Koide, K.; Hasegawa, Y.; Ito, H. Nat. Commun. 2019, 10, 111.
2. Seo, T.; Ishiyama, T.; Kubota, K.; Ito, H. Chem. Sci. 2019, 10, 8202.
3. Seo, T.; Kubota, K.; Ito, H. J. Am. Chem. Soc. 2020, 142, 9884.
4. Seo, T.; Toyoshima, N; Kubota, K.; Ito, H. J. Am. Chem. Soc. 2021, 143, 6165.
5. Takahashi, R.; Hu, A.; Gao, P.; Gao, Y.; Pang, Y.; Seo, T.; Maeda, S.; Jiang, J.; Takaya, H.;
Kubota, K.; Ito, H. Nat. Commun. 2021, 12, 6691.
6. Kubota, K.; Pang, Y.; Miura, A.; Ito, H. Science 2019, 366, 1500.
7. Pang, Y.; Lee, J. W.; Kubota, K.; Ito, H. Angew. Chem. Int. Ed. 2020, 59, 22570.
8. Kubota, K.; Toyoshima, N.; Miura, D.; Jiang, J.; Maeda, S.; Jin, M.; Ito, H.
Angew. Chem. Int. Ed. 2021, 60, 16003.

Imaging the mechano-chemical feedbacks in biological patterning
Fumio Motegi
Professor
Affiliation: Institute for Genetic Medicine, Hokkaido University,
Sapporo, Hokkaido, 060-0815 Japan
E-mail:motegi@igm.hokudai.ac.jp
Specialty: Cell Biology, Developmental biology, Mechanobiology

ABSTRACT
Cell polarity is necessary for diverse processes during development and prevents
progression of cancer and ageing. A hallmark of polarized metazoan cells is the segregation ofpartitioning-defective (PAR) proteins into distinct compartments at the cell cortex. However, thedesign principle that governs local interactions among PAR proteins into global cellular patterning
remains elusive. Using Caenorhabditis elegans zygotes as a model system, my group uncovered
1) the mechanisms underlying symmetry breaking by sperm-donated centrosome1,2,3,6, and 2) how
physical properties of the cell cortex ensures asymmetric segregation of PAR proteins4. Based on
the core molecular players and interactions in zygotes, we re-constructed the pattern-forming
circuits of PAR polarity network in apolar blastomeres7 and non-metazoan yeast cells5. Ourfindings provide the simplest network that executes self-organizing polarization, which will
permit synthetic control of the cell polarity program in living organisms.
REF.
[1] Nature Cell Biology 8, 978-985 (2006)[2] Development 137, 1669-1677 (2010)[3] Nature Cell Biology 13, 1361-1367 (2011)[4] Nature Cell Biology 19, 988-995 (2017)[5] Nature Chemical Biology 14, 917-927 (2018)[6] Developmental Cell 48, 631-645 (2019)[7] Cell Reports 36, 109326 (2021)

Structural bioinformatics studies of viral proteins
Manabu Igarashi
Associate Professor
Affiliation: International Institute for Zoonosis Control, Hokkaido
University
e-mail: igarashii@czc.hokudai.ac.jp
Specialty: Computational Chemistry and Biology
ABSTRACT
Hemorrhagic fever viruses (HFVs), such as filovirus, arenavirus, nairovirus, arezoonotic pathogens that often cause severe, life-threatening disease in human. There are only a
few approved therapeutics for these viral diseases. Although it has been reported that some
antiviral drugs such as nucleotide analogues clinically used for other viral diseases may also bebeneficial against these viruses, their efficacies have not been fully proven and concerns about
adverse effects remain. Thus, the development of effective and safe therapeutics against
hemorrhagic fever viruses is of crucial importance.
The present work was designed with two broad research objectives. Firstly, we aimed
to computationally predict and experimentally evaluate the functionally relevant sites required for
replication of hemorrhagic fever viruses, particularly the Ebola virus (EBOV). Secondly, as acomplement to the previous objective, we focused on the design of small compounds that could
target these functional regions of viral proteins.
In order to predict protein interaction sites on viral proteins, we analyzed 3D structuresof viral proteins by using patch analysis. We found a novel hydrophobic patch on the surface of
the EBOV VP35 protein and predicted that this patch is a functionally important site. Although
the functionally important sites on viral proteins might be potential targets for antiviral drug
discovery, most of these sites are likely to be engaged in protein-protein interactions (PPIs).
Generally, targeting PPIs with small molecules is challenging, since protein–protein interfaces
are relatively large, flat areas (1500–3000 Å2). In this study, we explored the suitability of using
small compounds to inhibit PPIs in viral proteins using several computational approaches.
Druggable sites, or sites engaged in PPIs, on EBOV proteins were predicted using a combinationof patch and cavity analyses. One site on VP35 was selected as a target for antiviral drug discovery.
The other method, mixed solvent molecular dynamics simulation, confirmed the druggable site
on VP35. We then performed in silico screening of 5,597 compounds from DrugBank, and the
top-ranked compounds were selected for an in vitro assay. In this presentation, we will discuss
the results obtained through our current analyses.
REF.
1. M. Igarashi, T. Hirokawa, Y. Takadate, A. Takada, Structural Insights into the Interaction of
Filovirus Glycoproteins with the Endosomal Receptor Niemann-Pick C1: A Computational Study,
Viruses, 13(5), 913 (2021)
2. N Kasajima, K Matsuno, H Miyamoto, M Kajihara, M Igarashi, A Takada, Functional
Importance of Hydrophobic Patches on the Ebola Virus VP35 IFN-Inhibitory Domain, Viruses,
13(11), 2316 (2021)
3. M. Igarashi, K. Matsuno, A. Takada, T. Hirokawa, manuscript in preparation.

Physics of gene regulation – Looking at genome from the viewpoint of soft matterphysics and chemical reaction researches
Tetsuya Yamamoto
Specially Appointed Associated Professor
Affiliation: ICReDD, Hokkaido University and PRESTO, JSTe-mail: tyamamoto@icredd.hokudai.ac.jp
Specialty: Biological physics and soft matter physics
ABSTRACT
Recent development of experimental techniques, such as super-resolution microscopes andnext generation sequencing techniques, has revealed highly organized structure of genome in the
nucleus of eukaryotic cell. The structure and dynamics of genome is involved in the regulationof gene expression. For example, heterochromatin, which is highly condensed region ofchromatin, coexists with euchromatin, which is relatively dilute region of chromatin1.
Transcription, which is the first step of gene expression and is one of the important chemical
reactions in life, is suppressed in heterochromain and is active in euchromatin. Enhancers,which are regulatory DNA sequences, are located at a few kb away from promoters of the target
genes. Transcription machineries, such as RNA polymerase II, transcription factors, andmediators, form so-called transcriptional condensates by liquid-liquid phase separation andenhancers regulate the expression of target genes by localizing these genes at the proximity to thetranscriptional condensates2. The structure and dynamics of genome as well as nuclear bodies,such as transcriptional condensates, can be treated by using an extension of soft matter physics.
It is of interest to understand how gene expression is regulated by the structure and dynamics ofgenome as well as the interaction with other structures in nucleus, such as nuclear bodies andnuclear membranes, by combining the soft matter physics and the kinetics of biochemical
reactions involved in gene expression.
In this talk, I will introduce our attempt to understand the mechanism of the assembly ofparaspeckles3-4 by the fusion research between (experimental) RNA molecular biology and(theoretical) soft matter physics, as an example of providing new understanding of biologicalsystems by looking these systems from the viewpoint of soft matter physics and chemical reaction
researches.
REF.
1. T. Yamamoto, T. Sakaue, and H. Schiessel, Phase separation of chromatin brush driven by
enzymatic reaction dynamics of histone posttranslational modifications, bioRxiv, (2020).
2. T. Yamamoto, T. Sakaue, and H. Schiessel, Slow chromatin dynamics enhances promoter
accessibility to transcriptional condensates, Nucleic Acids Research, 49, 5017-5027 (2021).
3. T. Yamamoto, T. Yamazaki, and T. Hirose, Phase separation driven by production ofarchitectural RNA transcripts, Soft Matter, 16, 4692-4698 (2020).
4. T. Yamazaki, T. Yamamoto, H. Yoshino, S. Souquere, S. Nakagawa, G. Pierron, T. Hirose,
Paraspeckles are constructed as block copolymer micelles. EMBO J, e107270 (2021).