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 have found applications in chemistry, such as the first deep learning GAN used in anti-cancer compound design.
“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).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [95]
The Introduction and Results discuss virtual screening libraries and molecular docking, not GANs. Searches for GANs, deep learning, and anti-cancer compound design found no passage addressing the claimed application or its priority. (LLM, conf=0.76)
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- 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 · 2022: 1
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › 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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