ArticleJournal of computer-aided molecular design2026
Explainable QSAR models of 5-HT1A receptor ligands using conceptual DFT descriptors and no-code machine learning tools.
Article in Journal of computer-aided molecular design, 2026. 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
Quantitative structure-activity relationship (QSAR) modeling is a central component of molecular informatics and ligand-based drug discovery, enabling the analysis and interpretation of structure-activity relationships from molecular descriptors. In this study, mechanistically interpretable binary QSAR models were developed to classify the activity of 89 serotonin 5-HT1A receptor ligands by integrating Conceptual Density Functional Theory (CDFT) descriptors with no-code machine learning workflows. Neutral and protonated molecular forms were considered under both vacuum and aqueous conditions, and electronic reactivity indices (η, μ, ω, and ΔN
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