Journal
JOURNAL OF MEDICINAL CHEMISTRY
Volume 63, Issue 16, Pages 8705-8722Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jmedchem.0c00385
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Funding
- Arnold and Mabel Beckman Foundation
- Chan Zuckerberg Initiative DAF
- Silicon Valley Community Foundation [2018-191905]
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The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish quantitative structure-activity and structure-property relationships for drug discovery. Now, advances in deep learning make it possible to efficiently and compactly learn molecular representations directly from data. In this review, we discuss how active research in molecular deep learning can address limitations of current descriptors and fingerprints while creating new opportunities in cheminformatics and virtual screening. We provide a concise overview of the role of representations in cheminformatics, key concepts in deep learning, and argue that learning representations provides a way forward to improve the predictive modeling of small molecule bioactivities and properties.
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