De novo in silico molecule modeling with a reliable AI system

Research Project

Project/Area Number	23K16942
Research Category	Grant-in-Aid for Early-Career Scientists
Allocation Type	Multi-year Fund
Review Section	Basic Section 61030:Intelligent informatics-related
Research Institution	The University of Tokyo
Principal Investigator	ディープテシュダス東京大学, 大学院新領域創成科学研究科, 特任研究員 (60809367)
Project Period (FY)	2023-04-01 – 2025-03-31
Project Status	Granted (Fiscal Year 2023)
Budget Amount *help	¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000) Fiscal Year 2024: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000) Fiscal Year 2023: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Keywords	Interpretability / Conformal Prediction / Uncertainty modeling / Generate molecule
Outline of Research at the Start	In this project, we will develop a machine learning framework and exploit the conformal prediction (CP) framework to quantify the uncertainty in in-silico molecule modeling.