ArticleFrontiers in chemistry2025
Hydrogen-centric machine learning approach for analyzing properties of tricyclic anti-depressant drugs.
Article in Frontiers in chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Integrating Machine Learning Approaches and Topological Indices for Prediction of Electronic Properties in Anticancer Drugs.ACS omega · 2026Article
- Mathematical Modeling and Computation of NM-Polynomial Indices for Physicochemical Properties Prediction.Scientific reports · 2026Article
- Eccentricity-based topological indices of QSPR modelling using anti-cancer drugs.Frontiers in chemistry · 2026Article
- QSPR analysis of the drugs used to treat major depressive disorder using degree and neighborhood degree based indices.Frontiers in chemistry · 2026Article
- Topological insights into breast cancer drugs: a QSPR approach using resolving topological indices.Frontiers in chemistry · 2025Article
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2 authors.
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Abstract
Introduction: Tricyclic anti-depressant (TCA) drugs are widely used to treat depression, but traditional methods for evaluating their physicochemical properties can be time-consuming and costly. This study examines how topological indices can help to predict the properties of TCA drugs, with a special focus on the role of the hydrogen representation. Methods: Two molecular configurations were analyzed: one with only explicit hydrogen and the other including all hydrogen atoms. To assess predictive performance, linear regression (LR) and support vector regression (SVR) models were employed. Results: The results showed that adding all hydrogen atoms showed strong correlations, especially for polarizability, molar refractivity, and molar volume. Among the models employed, SVR provided more accurate results. Additionally, hydrogen representation had a stronger impact on SVR's predictions. Discussion: These findings highlight the potential of using machine learning techniques in quantitative structure-property relationship (QSPR) models for more efficient and reliable predictions of drug properties.
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