ArticlePharmacological reports : PR2023
Automated machine learning approach for developing a quantitative structure-activity relationship model for cardiac steroid inhibition of Na
Article in Pharmacological reports : PR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundQuantitative structure-activity relationship (QSAR) modeling is a method of characterizing the relationship between chemical structures and biological activity. Automated machine learning enables computers to learn from large datasets and can be used for chemoinformatics. Cardiac steroids (CSs) inhibit the activity of Na
methodsThe chemical structures and inhibitory activities of 215 CS derivatives were obtained from the scientific literature. Predictive QSAR models were constructed using molecular descriptors, fingerprints, and biological activities.
resultsThe best predictive QSAR models were selected based on the LogLoss value. Using these models, the Matthews correlation coefficient, F1 score, and area under the curve of the test dataset were 0.6729, 0.8813, and 0.8812, respectively. Next, we showed automated construction of the predictive models for CS derivatives, which may be useful for identifying novel CSs suitable for candidate drug development.
conclusionThe automated machine learning-based QSAR method developed here should be applicable for the time-efficient construction of predictive models using only a small number of compounds.
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