| Budget Amount *help |
¥76,180,000 (Direct Cost: ¥58,600,000、Indirect Cost: ¥17,580,000)
Fiscal Year 2021: ¥15,600,000 (Direct Cost: ¥12,000,000、Indirect Cost: ¥3,600,000)
Fiscal Year 2020: ¥15,600,000 (Direct Cost: ¥12,000,000、Indirect Cost: ¥3,600,000)
Fiscal Year 2019: ¥15,600,000 (Direct Cost: ¥12,000,000、Indirect Cost: ¥3,600,000)
Fiscal Year 2018: ¥15,600,000 (Direct Cost: ¥12,000,000、Indirect Cost: ¥3,600,000)
Fiscal Year 2017: ¥13,780,000 (Direct Cost: ¥10,600,000、Indirect Cost: ¥3,180,000)
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| Outline of Final Research Achievements |
The purpose of this research is to develop a model that represents a wide variety of chemical communication in a unified manner. We have developed the next-generation COPICAT, which is a virtual screening system that comprehensively and highly accurately predicts protein-compound interactions, and achieved higher accuracy than the state-of-the-art existing methods. We have developed a variational auto-encoder (NP-VAE) for handling natural compounds and succeeded in acquiring a chemical latent space that encodes natural macromolecular structures. A latent space of natural compounds and macromolecular structures was constructed using 1,900 types of compound data provided from the members of this research project. We succeeded in discovering a large number of new PKC ligand candidates through machine learning and expert domain knowledge feedback strategies.
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