MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action
Open accessAbstract
To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is needed. In contrast, extant predictive methods do not comprehensively address these aspects of drug discovery and rely on features derived from extensive, often unavailable experimental information for novel molecules. To address these issues, we developed MEDICASCY, a multilabel-based boosted random forest machine learning method that only requires the small molecule's chemical structure for the drug side effect, indication, efficacy, and probable mode of action target predictions; however, it has comparable or even significantly better performance than existing approaches requiring far more information. In retrospective benchmarking on high confidence predictions, MEDICASCY shows about 78% precision and recall for predicting at least one severe side effect and 72% precision drug efficacy. Experimental validation of MEDICASCY's efficacy predictions on novel molecules shows close to 80% precision for the inhibition of growth in ovarian, breast, and prostate cancer cell lines. Thus, MEDICASCY should improve the success rate for new drug approval. A web service for academic users is available at http://pwp.gatech.edu/cssb/MEDICASCY.
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How inclusion of higher quality structures improves the ability to predict drug side effects, efficacy and the role of missense variations in non-Mendelian diseases depends on accurately characterizing the interplay of many molecular players.
“How inclusion of higher quality structures improves the ability to predict drug side effects, efficacy64 and the role of missense variations in non-Mendelian diseases depends on accurately characterizing the interplay of many molecular players.”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [64]
Knowledge of a protein's structure alone does not provide its biochemical, much less phenotypical effects, but structure can be a component.
“Importantly, while knowledge of a protein’s structure alone does not provide its biochemical, much less, phenotypical effects, structure can be a component.64”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [64]
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- [25]Zhou H; Gao M; Skolnick J, Comprehensive prediction of drug-protein interactions and side effects for the human proteome. Nature Scientific Reports 2015, 5, 11090. PMC4603786 26057345
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Self-citation
- Authors
- Zhou, H., Cao, H., Matyunina, L., Shelby, M., Cassels, L., McDonald, J. F., & Skolnick, J.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Skolnick, Jeffrey
- Venue
- Molecular Pharmaceutics
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-04-02
- Volume
- 17
- Issue
- 5
- Pages
- 1558-1574
- ISSN
- 1543-8384 (ISSN-L)
- 1543-8392
- PMC
- PMC7319183
- OpenAlex
- W3014239097
- Cited by
- 39
- References
- 44
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 5 · 2023: 11 · 2022: 5 · 2021: 5 · 2020: 1
- FWCI
- 2.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Molecular Pharmaceutics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (1R35GM-118039)
- 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 › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- drug side effect prediction, drug indications, drug efficacy prediction, small molecule drugs, multi-label classification, drug discovery, web services
- MeSH terms
- Machine Learning, Humans, Retrospective Studies, Benchmarking, Cell Line, Tumor, Drug Discovery, Drug-Related Side Effects and Adverse Reactions
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