The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Simple Selection Criteria for Drug-like Chemical Matter
Abstract
A simple pharmacophore point filter has been developed that discriminates between drug-like and nondrug-like chemical matter. It is based on the observation that nondrugs are often underfunctionalized. Therefore, a minimum count of well-defined pharmacophore points is required to pass the filter. The application of the filter results in 66-69% of subsets of the MDDR database to be classified as drug-like. Furthermore, 61-68% of subsets of the CMC database are classified as drug-like. In contrast, only 36% of the ACD are found to be drug-like. While these results are not quite as good as those obtained with recently described neural net approaches, the method used here has clear advantages. In contrast to a neural net approach and also in contrast to decision tree methods described recently, the pharmacophore filter has been developed by using "chemical wisdom" that is unbiased from fitting the structural content of specific drug databases to prediction models. Similar to decision tree methods, the pharmacophore point filter provides a detailed structural reason for the classification of each molecule as drug or nondrug. The pharmacophore point filter results are compared to neural net filter results. A statistically significant overlap between compounds recognized as drug-like validates both approaches. The pharmacophore point filter complements neural net approaches as well as property profiling approaches used as drug-likeness filters in compound library analysis and design.
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Cites
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Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367
Combining docking and molecular dynamic simulations in drug design
Alonso, H., Bliznyuk, A. A., & Gready, J. E. (2006) · Medicinal Research Reviews · doi:10.1002/med.20067 · Cited by 759
Deep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683
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Optimal Heck Cross‐Coupling Catalysis: A Pseudo‐Pharmaceutical Approach
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Metadata
- Authors
- Muegge, I., Heald, S. L., & Brittelli, D.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2001-05-15
- Volume
- 44
- Issue
- 12
- Pages
- 1841-1846
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2003076573
- Cited by
- 1,315
- References
- 21
- FWCI
- 20.35
- Type
- journal article
- Language
- en
- Open access
- closed
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