Naïve Bayesian Models for Vero Cell Cytotoxicity
Open accessAbstract
Purpose To advance translational research of potential therapeutic small molecules against infectious microbes, the compounds must display a relative lack of mammalian cell cytotoxicity. Vero cell cytotoxicity (CC 50) is a common initial assay for this metric. We explored the development of naïve Bayesian models that can enhance the probability of identifying non-cytotoxic compounds.Methods Vero cell cytotoxicity assays were identified in PubChem, reformatted, and curated to create a training set with 8741 unique small molecules. These data were used to develop Bayesian classifiers, which were assessed with internal cross-validation, external tests with a set of 193 compounds from our laboratory, and independent validation with an additional diverse set of 1609 unique compounds from PubChem.Results Evaluation with independent, external test and validation sets indicated that cytotoxicity Bayesian models constructed with the ECFP_6 descriptor were more accurate than those that used FCFP_6 fingerprints. The best cytotoxicity Bayesian model displayed predictive power in external evaluations, according to conventional and chance-corrected statistics, as well as enrichment factors.Conclusions The results from external tests demonstrate that our novel cytotoxicity Bayesian model displays sufficient predictive power to help guide translational research. To assist the chemical tool and drug discovery communities, our curated training set is being distributed as part of the Supplementary Material. Graphical Abstract Naive Bayesian models have been trained with publically available data and offer a useful tool for chemical biology and drug discovery to select for small molecules with a high probability of exhibiting acceptably low Vero cell cytotoxicity.
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The Bayesian approach has been widely applied to ADME properties by predicting aqueous solubility, mouse liver microsomal stability, Caco-2 cell permeability, cytotoxicity and interactions with transporters.
“This Bayesian approach has also been widely applied to ADME properties by predicting aqueous solubility, mouse liver microsomal stability42, Caco-2 cell permeability43, cytotoxicity44 and interactions with transporters45.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [44]
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- Authors
- Perryman, A. L., Patel, J. S., Russo, R., Singleton, E., Connell, N., Ekins, S., & Freundlich, J. S.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Collaborations Pharmaceuticals (United States) (US)
- Corresponding author
- Freundlich, Joel S.
- Venue
- Pharmaceutical Research
- Published
- 2018-06-29
- Volume
- 35
- Issue
- 9
- Pages
- 170-170
- ISSN
- 0724-8741 (ISSN-L)
- 1573-904X
- PMC
- PMC7768703
- OpenAlex
- W2810207189
- Cited by
- 33
- References
- 41
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 2 · 2023: 3 · 2022: 7 · 2021: 8 · 2020: 3 · 2019: 2
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Pharmaceutical Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (U19AI109713)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Keywords
- Vero cell cytotoxicity, naive Bayesian classification, chemical safety screening, drug discovery, chemical biology
- MeSH terms
- Animals, Bayes Theorem, Chlorocebus aethiops, Models, Biological, Models, Molecular, Vero Cells, Information Storage and Retrieval, Toxicity Tests, Small Molecule Libraries, Drug Discovery, Databases, Pharmaceutical
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