J. Biol. Macromol., 19 (1), 5-10 (2019)
ReviewDevelopment of Practical Artificial Intelligence System for Drug Discovery and Its Application toActivity Prediction of Small Molecule Protein-Protein Interaction Modulators
Hirotsugu Komatsu*, Ken Ikeda, Takeshi Tanaka and Takao Matsuzaki
R&D and BD Division, Interprotein Corporation, SIC-2-205, 5-4-30 Nishi-hashimoto, Midori-ku,Sagamihara, 252-0131, Japan
Received May 13, 2019; Accepted May 23, 2019

Degree of attention to protein-protein interaction (PPI) are increasing under circumstances ofthe exhaustion of conventional drug targets such as enzymes, receptors and channels whose ligands
are non-protein.

However, identification of potent small molecule PPI modulators are stillchallenging for pharmaceutical industries because in-house chemical libraries of pharmaceuticalcompanies do not include strong and specific binders to PPI targets, and it is difficult to identify
active compounds by high-throughput screening (HTS).

On the other hand, to improve low

productivity of drug research and developments (R&D), which is one of the most important longstanding issues for pharmaceutical industries, application of artificial intelligence (AI) to many
stages of drug R&D is being tried by large number of pharmaceutical companies, academic
institutions and biotech companies.

However, a part of researchers of pharmaceutical companies

also have opinions with a skeptic tone for the contribution of AI to the improvement of low
productivity of drug R&D.

Based on these situations, we review current approaches of applicationof AI to drug discovery researches and introduce Interprotein’s approach to identify potent small
molecule PPI modulators with an AI-based activity prediction system.
Key words: protein-protein interaction, in silico screening, artificial intelligence, activity prediction,ubiquitin-proteasome system
Introduction

and approved by regulatory agencies such as US

In a couple of past decades, many challenging

Food and Drug Administration (FDA), European

struggles have revealed that small molecules canMedicines Agency (EMEA), and Pharmaceuticals

act as effective protein-protein interaction (PPI)and Medical Devices Agency (PMDA) of Japan.

modulators although, prior to that, PPIFig. 1 shows representative approved PPI

modulation by small molecules had beenmodulators which exclude conventional PPI

considered to be substantially impossible.

A partstabilizers such as FK506, rapamycin andof potent and specific small molecule PPIpaclitaxel.

modulators was evaluated for clinical availability

potentially orally active small molecules with*

Correspondence author: Hirotsugu Komatsu
E-mail: komatsu@interprotein.com

As an index for identification ofgood pharmacokinetic profiles, the Lipinski’s ruleof five is widely used by many medicinal

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Practical AI system and PPI modulators

Fig. 1. Representative approved small molecule PPI modulators.
chemists.

In this rule, it is recommended thatstructure-based and ligand-based approaches, andmolecular weight should be less than 500.

most of all AI-based drug discovery biotech

Interestingly, however, molecular weights of 7

companies seem to conduct the activity prediction

compounds out of 9 molecules listed in Fig. 1

by ligand-based approaches.

were more than 500, which suggests that small

current ligand-based approaches are roughly

molecule PPI modulators have a need for higher

categorized into an analysis of advanced

molecular weight than conventional orally active

quantitative structure-activity relationshipsmall molecule drugs.

(QSAR), but a part of researches involved in drug

Thus, the fact that many

In addition, many

approved small molecule PPI modulators are indiscovery have an impression that QSAR-based

violation of the Lipinski’s rule of five might

approaches do not well function in many cases forindicate that regulation of PPIs by smallPPIs.

molecules is a still challenging approach.

On theBased on these situations, we tried todevelop a practical AI system for drug discovery

other hand, to improve the efficiency andand apply it to identification of potent small

provability of drug discovery of small molecule

molecule PPI modulators.

PPI modulators, application of artificial
intelligence (AI)-based technologies is expected.

Outline of Interprotein’s approach for AI-However, current AI-based technologies including

based drug discovery

activity prediction are mainly used for

One of the major problems for pharmaceutical

identification of non-PPI-targeted compounds.

companies is low productivity of drug researchAI-based drug discovery technologies for activity

and development (R&D), and the purpose ofprediction of small molecules are divided into

application of AI to drug R&D is to improve the7

H. Komatsu et al.

Table 1.

productivity.

Differences between main current AI-based drug discovery technologies and AIguided INTENDD from the aspect of activity prediction of small molecules.

The major cause of the low

prediction of small molecules.

Approach of AI-productivity is mainly failures in phase II studies,

guided INTENDD is purely structure-based,

which is often called phase II attrition [1], and thewhich enables to newly identify active

biggest reason for the failure is lack of efficacy

compounds without pre-reported active ligand[2]. These facts suggest that the most important

information.

factors in drug R&D are “validity of concept

and atomic-level (but not partial structure-level)(including selection of drug target)” andinteractions between target proteins and“selection of the compound that shows efficacy incompounds with known bioactivities.

human at a high probability”.

training data are produced by proprietary data-Almost all biotech

Unique training data represent 3D-Suchcompanies conducting AI-based drug discovery
are comprehensively supporting selection of newdrug candidates including toxicological andpharmacokinetic aspects.

On the other hand,Interprotein is focusing on “prediction of activity
for human proteins” based on the abovementioned view point.

On the basis of thisconcept, Interprotein established a new AIimplemented activity prediction system andnamed it AI-guided INTerprotein’s Engine for
New Drug Design (INTENDD).

AI-guided

INTENDD was constructed based on theaccumulation of the many experiences andsuccessful results obtained from the examination
with INTENDD as a basal technology, which is abinding mechanism-based proprietary in silico
screening system discriminated from conventionalbinding energy-based docking methodologies.
Table 1 shows the differences between maincurrent AI-based drug discovery technologies andAI-guided INTENDD from the aspect of activity

Fig. 2. Comparison between actual andpredicted activities of BRD4
inhibitors.

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Practical AI system and PPI modulators

preprocessing method.

Predicted activity can be

Other 3 compounds also showed good

divided into 8 classes based on the order of active

correspondence between actual activities andconcentration.

predicted classes.

Furthermore, its specialty fields

include challenging drug targets such as PPIs andubiquitin-proteasome system-related proteins.

2) Activity prediction of compounds without
co-crystal structure information

Application of AI-guided INTENDD to PPI

Although the results of the confirmative study

targets

described above were satisfactory, it is rare that

1) Confirmatory study with public

co-crystal structure information has been clarified

information on co-crystal structure andbefore activity prediction in the practical drug

activity

discovery researches.

As an example of activity prediction of PPI

predict activities of compounds without co-crystal

Therefore, we tried toinhibitors, we conducted a confirmatory study

structure information using small moleculewith public data on co-crystal structure (Protein

inhibitors for Runx1, which is a transcription

Data Bank, PDB) and activity (PDBbind) offactor mainly related to pathophysiology of acutebromodomain-containing protein 4 (BRD4)

myelocytic leukemia (AML) and functions byinhibitors (Fig. 2).

association with CBFβ.

When we compared theWhen we classified

activities of 4 compounds between actualactivity of 142 compounds into 8 classes, a goodPDBbind and AI-guided INTENDD prediction

consistence was observed between actual anddata, the both values showed a good consistency;

predicted activities (Fig. 3). Actual inhibitorye.g., while actual Kd value of the compound ofactivities for the Runx1/CBFβ – DNA binding

4QB3.pdb was 3.4 µmol/L, AI-guided INTENDD

were assessed by a standard surface plasmon

predicted that the activity was classified intoresonance (SPR) method and indicated on the“Class 4 (1 – 10 µmol/L)”, indicating that thehorizontal axis, and predicted activities wereactual activity was included in the predicted class.

indicated on the vertical axis.

Fig. 3.

Dark grey-colored

Activity prediction of Runx1/CBFβ inhibitors by AI-guided INTENDD.

9H. Komatsu et al.

Fig. 4.

Structure and property of JQ1.

cells show that actual activity is included in theFig. 5.

predicted single activity class (defined as“Accurate Prediction”) and flames consisting of
dark gray- and light grey-colored cells (the range
consisting of 3 classes of the predicted single
activity class, one order more potent class and oneorder less potent class) were defined as “GoodPrediction”.

The result showed a rising positive

slope from bottom left to top right demonstrating
an overall correspondence between actual andpredicted activities.

Overall rates of “AccuratePrediction” and “Good Prediction” were 34% and79%, respectively.

Importantly, AI-guided

Comparison of proposed binding poseand predicted activity with actual
binding pose and activity of JQ1.

conducting many studies for hit identification
with INTENDD, which is a basal in silico
screening technology of AI-guided INTENDDand enables proposal of hit candidates with agood balance of enthalpic and entropic bindingenergy.

Because input parameters of INTENDD

are partly overlapping with those of AI-guided
INTENDD, it is expected that AI-guided
INTENDD can predict activity of the compoundwith a favorable balance of enthalpy and entropy.

INTENDD gave high Good Prediction rates ofmore than 90% for relatively potent (Class 3 and4) compounds.
3) Activity prediction of the compound with agood balance of enthalpy- and entropydriven binding energy
One of the recent strategies for lead
optimization is identification of the compoundwith a good balance of enthalpy and entropy
components for binding free energy, and many
drug discovery researchers consider that thecompound with a certain ratio of enthalpy andenthalpy gains (but not losses) is favorable [3].
Based on this strategy, we are currentlyFig. 6. Proposed and actual binding posesof JQ1 (overlaid structures).

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Practical AI system and PPI modulators

Thus, we confirmed whether AI-guidedof structure and activity of existing compounds.

INTENDD can give a precise activity prediction

Moreover, as another approach, advantage offor a well characterized BRD4 inhibitor, JQ1,

virtual compound libraries is being taken.

whose ΔH and -TΔS values are -6.80 and -3.31,

Reymond reported that the number of virtualrespectively [4], and considered to be almost ideal

compounds with selected existing partial(Fig. 4).

structures was estimated to be 166.4 billion when

Although co-crystal structures of JQ1

are reported as 4QZs.pdb and 3MXF.pdb, athe number of molecular-composing atoms wasbinding pose of JQ1 was proposed in the pocket

increased up to 17 types [7].

of other BRD4 structure from 6CJ2.pdb (co-proprietary data-preprocessing method andcrystal of BRD4 and JWG056).in a purposeful

resultant unique hyperparameters are expected tomanner, and predicted activity was Class 2 (10 –contribute to activity prediction of huge number

100 nmol/L) (Fig. 5).

of compounds and improvement of productivity

On the other hand, actualactivities (Kd values) corresponding to 4QZS.pdb

Interprotein’sof drug R&D.

and 3MXF.pdb were 49 [5] and 50 nmol/L [6],respectively.

In addition, when we compared the

proposed binding pose (on 6CJ2.pdb) with actual

References[1] Patel, D. D., Antoni, C., Freedmen, S. J.,

binding pose (4QZS.pdb and 3MXF.pdb), bothAntoni, C., Freedman, S. J., Levesque, M.

binding poses showed a good consistence except

C., and Sundy, J. S. (2017) J. Allergy Clin.

only direction of tertiary butyl group is different

Immunol., 140, 685-687.

between predicted and actual binding poses (Fig.
6).

These results strongly suggest that AI-guided

INTENDD enables activity prediction of thecompounds with a favorable balance of enthalpic
and entropic binding energy and contributes to[2] Harrison, R. K. (2016) Nat. Rev. Drug
Discov., 15, 817-818.
[3] Klebe, G. (2015) Nat. Rev. Drug Discov., 14,
95-110.
[4] Tanaka, M., Roberts, J. M., Seo, H.-S.,

efficient identification of optimized smallSouza, A., Paulk., J., Scott, T. G., DeAngelo,molecule PPI modulators.

S. L., Dhe-Paganon, S., and Bradner, J. E.
(2016) Nat. Chem. Biol., 12, 1089–1096.

Future vision[5] Mishra, N. K., Urick, A. K., Ember, S. W. J.,

Interprotein is currently conducting in silico

Schönbrunn, E., and Pomerantz, W. C.

screening from around 10 million compounds that(2014) ACS Chem. Biol., 9, 2755-2760.

we can readily purchase in Japan, and based on[6] Filippakopoulos, P., Qi, J., Picaud, S., Shen,our experience, the potency of hit compounds

Y., Smith, W. B., Fedorov, O., Morse, E. M.,

seems to be limited to 100 nmol/L order in theKeates, T., Hickman, T. T., Felletar, I.,

best case.

Philpott, M., Munro, S., McKeown, M. R.,

For promotion of the process for lead

optimization, it is preferable to obtain more potent

Wang, Y., Christie, A. L., West, N.,

compounds as early as possible, but such

Cameron, M. J., Schwartz, B., Heightman, T.

compounds are rarely included in theD., La Thangue, N., French, C. A., Wiest, O.,commercially available real compound libraries.

Kung, A. L., Knapp, S., and Bradner, J. E.

To solve this problem, we are currently trying to(2010) Nature, 468, 1067-1073.

establish an AI system which proposes novelcompounds by de novo drug design independently[7] Reymond, J.-L. (2015) Acc. Chem. Res., 48,722−730.
Communicated by Sumio Ishijima