ORIGINAL RESEARCH article
Front. Chem.
Sec. Theoretical and Computational Chemistry
Volume 13 - 2025 | doi: 10.3389/fchem.2025.1603948
Hydrogen-Centric Machine Learning Approach for Analyzing Properties of Tricyclic Anti-depressant Drugs
Provisionally accepted
Ravi Sankar J.*
Simran Kour- VIT University, Vellore, India
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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. Two molecular configurations were analyzed: one with only explicit hydrogen and the other including all hydrogen atoms. The results showed that adding all hydrogen atoms showed strong correlations, especially for polarizability, molar refractivity, and molar volume. To assess predictive performance, linear regression (LR) and support vector regression (SVR) models were employed, with SVR providing more accurate results. Additionally, hydrogen representation had a stronger impact on SVR's predictions. 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.
Keywords: Tricyclic Anti-depressant Drugs, Topological indices, QSPR, linear regression, Support vector regression
Received: 01 Apr 2025; Accepted: 15 May 2025.
Copyright: © 2025 J. and Kour. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ravi Sankar J., VIT University, Vellore, India
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