A Scoring Scheme for Discriminating between Drugs and Nondrugs
Abstract
A scoring scheme for the rapid and automatic classification of molecules into drugs and nondrugs was developed. The method is a valuable new tool that can aid in the selection and prioritization of compounds from large compound collections for purchase or biological testing and that can replace a considerable amount of laborious manual work by a more unbiased approach. It is based on the extraction of knowledge from large databases of drugs and nondrugs. The method was set up by using atom type descriptors for encoding the molecular structures and by training a feedforward neural network for classifying the molecules. It was parametrized and validated by using large databases of drugs and nondrugs (169 331 molecules from the Available Chemicals Directory, ACD, and 38 416 molecules from the World Drug Index, WDI). The method revealed features in the molecular descriptors that either qualify or disqualify a molecule for being a drug and classified 83% of the ACD and 77% of the WDI adequately.
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The 'drug' quality is not an intrinsic property of chemicals since regulatory agencies and humans approve compounds for medicinal use.
“The 'drug' quality is not an intrinsic property of chemicals since regulatory agencies and, implicitly, humans approve compounds for medicinal use (29).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [29]
ML-based drug-likeness quantification is an integral part of the drug discovery process, with nearly 20 independent papers dedicated to drug-likeness discussed elsewhere.
“Despite these caveats, ML-based drug-likeness quantification is an integral part of the drug discovery process, with nearly 20 independent papers dedicated to druglikeness discussed elsewhere (29).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [29]
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- Authors
- Sadowski, J., & Kubinyi, H.
- Affiliations
- BASF (Germany) (DE)
- ORCID
- Sadowski, Jens
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 1998-08-01
- Volume
- 41
- Issue
- 18
- Pages
- 3325-3329
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2010595835
- Cited by
- 392
- References
- 4
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 5 · 2023: 5 · 2022: 5 · 2021: 5 · 2020: 5 · 2019: 7 · 2018: 6 · 2017: 6 · 2016: 6 · 2015: 5 · 2014: 5 · 2013: 4 · 2012: 7
- FWCI
- 40.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medicinal Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › History and advancements in chemistry
- Keywords
- drug-likeness prediction, scoring system, molecular descriptors, feedforward neural networks, compound prioritization
- MeSH terms
- Pharmaceutical Preparations, Drug Design, Databases, Factual, Neural Networks, Computer
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