期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 62, 期 3, 页码 463-471出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.1c01531
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资金
- National Institutes for Health [R35 GM134864]
- National Science Foundation [2040667]
- Passan Foundation
- Innovation and Technology Ecosystems
- Dir for Tech, Innovation, & Partnerships [2040667] Funding Source: National Science Foundation
This study finds that deep learning-based methods for predicting binding affinities lack generalizability, and a newly developed predictor, Yuel, shows better ability in predicting interactions between unknown compounds and proteins.
Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening and drug target identification. Deep learning-based approaches have recently been adapted to predict binding affinities and they claim to achieve high prediction accuracy in their tests; we show that these approaches do not generalize, that is, they fail to predict interactions between unknown proteins and unknown small molecules. To address these shortcomings, we develop a new compound-protein interaction predictor, Yuel, which predicts compound-protein interactions with a higher generalizability than the existing methods. Upon comprehensive tests on various data sets, we find that out of all the deep-learning approaches surveyed, Yuel manifests the best ability to predict interactions between unknown compounds and unknown proteins.
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