期刊
ACS CENTRAL SCIENCE
卷 4, 期 11, 页码 1520-1530出版社
AMER CHEMICAL SOC
DOI: 10.1021/acscentsci.8b00507
关键词
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资金
- Blue Waters Graduate Fellowship
- National Natural Science Foundation of China [11501306]
- Fok Ying-Tong Education Foundation [161003]
- Fannie and John Hertz Foundation
- NIH [R01 GM062868, U19 AI109662]
The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. The key parameters range from solubility (angstroms) to protein-ligand binding (nanometers) to in vivo toxicity (meters). Through feature learning. instead of feature engineering. deep neural networks promise to outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent to drug discovery. To this end, we present the PotentialNet family of graph convolutions. These models are specifically designed for and achieve state-of-the-art performance for protein-ligand binding affinity. We further validate these deep neural networks by setting new standards of performance in several ligand-based tasks. In parallel, we introduce a new metric, the Regression Enrichment Factor EF chi(R), to measure the early enrichment of computational models for chemical data. Finally, we introduce a cross-validation strategy based on structural homology clustering that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks.
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