Numerous drug discovery databases, such as ChEMBL and PubChem, have been developed and made publicly available, which provide data on activities of various drug discovery targets, toxicity, and pharmacokinetics. With the graph convolutional neural networks, kGCN (J. Cheminform., 2020, 12, 32), as the core developed at the Kyoto University, we are analyzing these enormous amount of data and building various AI prediction systems.
Small-molecule drugs have been designed by humans in most cases. To support drug design, we are conducting research to make AI generate more diverse molecules with more desirable properties. For this purpose, we have used an enormous number of chemical structures stored in public databases to teach AI knowledge of chemistry for generating chemical structures. In addition, we are developing AI to efficiently generate molecular structures with more desirable properties using various prediction systems (J. Chem. Inf. Model., 2022, accepted).
Nucleic acid drugs are attracting attention as a new modality, and several nucleic acid drugs have been approved and used as drugs. We have created a database of nucleic acid sequences with their activity and publish them for everyone’s free use (Nucleic Acid Res., 2021, 49, W193). This system also incorporates an AI system that predicts the activity of any given nucleic acid sequence.
Drugs exert their pharmacological activity by binding to the target protein. Upon binding, water molecules around the protein (hydrated waters) must be replaced by the drug. Therefore, the ease of replacement of hydrated waters is very important for the drug to bind and exhibit its activity.By using 3D-RISM theory, it becomes possible to estimate the hydration state of proteins, which is critical for drug binding.
We used the supercomputers of RIKEN to comprehensively simulate the hydration state of 3,706 proteins using the 3D-RISM theory (J. Comput. Chem., 2020, 41, 2406). Based on the data of the hydration states of 3,706 proteins, we have developed a methodology for quickly predicting the hydration state of any proteins using AI (J. Chem. Inf. Model., 2022, 62, 4460). As a result, 3D-RISM calculations that used to take an average of several hours on a supercomputer can now be completed in about tens of seconds by AI. As a further development, we are developing an AI that uses hydration state information to predict binding affinities and correct binding modes.
Antibody drugs are a source of innovative medicines. In developing antibody drugs, after obtaining an active lead antibody, amino acids are mutated to improve its molecular properties such as activity and stability. Normally, one amino acid is mutated; the changes in its activity and properties are observed; and the good mutations are combined.
We have developed a new method of mutating two amino acids simultaneously (double-point mutant) using simulation technologies (Scientific Reports, 2020, 10, 17590). In this method, a huge number of 3D models of the double-point mutant should be generated and evaluated for improvement of properties based on their 3D structures. Currently, generating 3D models of large number of double-point mutants is automated; however, we are further refining and improving the method using AI. The process of selecting mutants expected to improve activity from among numerous 3D models has been done by researchers visually inspecting the 3D models, but we are working on automating it with AI.
The business development office handles alliances for collaboration with companies, universities, and external research institutes. It is also involved in licensing intellectual property rights to companies in collaboration with RIKEN Innovation.
Mission of Translational Research Office (TRO), the competent team which consists of number of experts of translational research (TR) from industry, academic TR institutes, regulatory organizations, is to support projects establishing clinical development plan.
