Kour Simran, Ravi Sankar J
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Front Chem. 2025 Jun 3;13:1603948. doi: 10.3389/fchem.2025.1603948. eCollection 2025.
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. To assess predictive performance, linear regression (LR) and support vector regression (SVR) models were employed.
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.
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.
三环抗抑郁药（TCA）被广泛用于治疗抑郁症，但评估其物理化学性质的传统方法可能既耗时又昂贵。本研究探讨拓扑指数如何有助于预测TCA药物的性质，特别关注氢表示法的作用。
分析了两种分子构型：一种仅含显式氢，另一种包含所有氢原子。为评估预测性能，采用了线性回归（LR）和支持向量回归（SVR）模型。
结果表明，添加所有氢原子显示出强相关性，尤其是对于极化率、摩尔折射率和摩尔体积。在所采用的模型中，SVR提供了更准确的结果。此外，氢表示法对SVR的预测有更强的影响。
这些发现突出了在定量构效关系（QSPR）模型中使用机器学习技术对药物性质进行更高效、可靠预测的潜力。
