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Modeling the expansion of virtual screening libraries
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Abstract
Recently, 'tangible' virtual libraries have made billions of molecules readily available. Prioritizing these molecules for synthesis and testing demands computational approaches, such as docking. Their success may depend on library diversity, their similarity to bio-like molecules and how receptor fit and artifacts change with library size. We compared a library of 3 million 'in-stock' molecules with billion-plus tangible libraries. The bias toward bio-like molecules in the tangible library decreases 19,000-fold versus those 'in-stock'. Similarly, thousands of high-ranking molecules, including experimental actives, from five ultra-large-library docking campaigns are also dissimilar to bio-like molecules. Meanwhile, better-fitting molecules are found as the library grows, with the score improving log-linearly with library size. Finally, as library size increases, so too do rare molecules that rank artifactually well. Although the nature of these artifacts changes from target to target, the expectation of their occurrence does not, and simple strategies can minimize their impact.
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“GANs (95), inspired by game theory and deep learning, have found applications in chemistry, such as the first deep learning GAN used in anti-cancer compound design (96).”
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Self-citation
Metadata
- Authors
- Lyu, J., Irwin, J. J., & Shoichet, B. K.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Irwin, John J.
- Shoichet, Brian K.
- Venue
- Nature Chemical Biology
- Published
- 2023-01-16
- Volume
- 19
- Issue
- 6
- Pages
- 712-718
- ISSN
- 1552-4450 (ISSN-L)
- 1552-4469
- PMC
- PMC10243288
- OpenAlex
- W4316507591
- Cited by
- 193
- References
- 53
- Citations by year
- 2026: 49 · 2025: 57 · 2024: 55 · 2023: 31
- FWCI
- 27.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Chemical Biology (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (GM133836, R35GM122481)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- library size, molecular diversity, molecular similarity, protein-ligand docking
- MeSH terms
- Libraries, Digital, Molecular Docking Simulation
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