Stochastic Voyages into Uncharted Chemical Space Produce a Representative Library of All Possible Drug-Like Compounds
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The "small molecule universe" (SMU), the set of all synthetically feasible organic molecules of 500 Da molecular weight or less, is estimated to contain over 10(60) structures, making exhaustive searches for structures of interest impractical. Here, we describe the construction of a "representative universal library" spanning the SMU that samples the full extent of feasible small molecule chemistries. This library was generated using the newly developed Algorithm for Chemical Space Exploration with Stochastic Search (ACSESS). ACSESS makes two important contributions to chemical space exploration: it allows the systematic search of the unexplored regions of the small molecule universe, and it facilitates the mining of chemical libraries that do not yet exist, providing a near-infinite source of diverse novel compounds.
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ML models, analysis of their key features, and dimensionality reduction represent important tools for building structure-property relationships and mapping chemical space.
“ML models, analysis of their key features (32,52), and dimensionality reduction (i.e., unsupervised learning) (53-56) represent important tools for building structure-property relationships and mapping chemical space (57).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [57]
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- Authors
- Virshup, A. M., Contreras-García, J., Wipf, P., Yang, W., & Beratan, D. N.
- Affiliations
- University of Pittsburgh (US)
- Center for Chemical Methodologies and Library Development, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
- Corresponding author
- Yang, Weitao
- Beratan, David N.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-05-02
- Volume
- 135
- Issue
- 19
- Pages
- 7296-7303
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- PMC
- PMC3670418
- OpenAlex
- W2110791536
- Cited by
- 294
- References
- 52
- Citations by year
- 2026: 9 · 2025: 24 · 2024: 28 · 2023: 21 · 2022: 25 · 2021: 38 · 2020: 34 · 2019: 19 · 2018: 10 · 2017: 19 · 2016: 18 · 2015: 28 · 2014: 17 · 2013: 4
- FWCI
- 24.45
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of the American Chemical Society (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (P50GM067082)
- 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 › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- chemical space exploration, stochastic search, drug-like molecules, synthetic feasibility, molecular diversity
- MeSH terms
- Algorithms, Stochastic Processes, Drug Design, Small Molecule Libraries
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