[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127237-en":3,"doc-seo-127237-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127237,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Hydrogen-centric machine learning approach for analyzing properties of tricyclic antidepressant drugs","This study develops a hydrogen-centric machine learning workflow to predict physicochemical properties of tricyclic antidepressant (TCA) drugs using quantitative structure-property relationship (QSPR) modeling. Two molecular representations are compared: explicit hydrogen only versus inclusion of all hydrogen atoms. Linear regression (LR) and support vector regression (SVR) are trained to relate topological indices to target properties. Results show that adding all hydrogen atoms strengthens correlations for polarizability, molar refractivity, and molar volume, and that SVR achieves the highest predictive accuracy with stronger sensitivity to hydrogen representation.","TYPE Original Research PUBLISHED 03 June 2025  \nDOI 10.3389/fchem.2025.1603948  \nOPEN ACCESS  \nEDITED BY  \nDejan Milenković,  \nUniversity of Kragujevac, Serbia  \nREVIEWED BY  \nRahul Pinjari,  \nSwami Ramanand Teerth Marathwada University, India  \nDapeng Wang,  \nChinese Academy of Sciences (CAS), China  \n*CORRESPONDENCE  \nJ. Ravi Sankar,  \n [ravisankar.j@vit.ac.in](ravisankar.j@vit.ac.in)  \nRECEIVED 01 April 2025  \nACCEPTED 15 May 2025  \nPUBLISHED 03 June 2025  \nCITATION  \nKour S and Ravi Sankar J (2025) Hydrogencentric machine learning approach for analyzing properties of tricyclic antidepressant drugs.  \nFront. Chem. 13:1603948 .  \ndoi: 10.3389/fchem.2025.1603948  \nCOPYRIGHT  \n© 2025 Kour and Ravi Sankar. This is an openaccess 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) and the copyright owner(s) 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.  \nHydrogen-centric machine learning approach for analyzing properties of tricyclic  \nanti-depressant drugs  \nSimran Kour and J. Ravi Sankar*  \nDepartment of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India  \nIntroduction: 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.  \nMethods: 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.  \nResults: 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.  \nDiscussion: 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.  \nKEYWORDS  \ntricyclic anti-depressant drugs, topological indices, QSPR, linear regression, support vector regression  \n1 Introduction  \nMental health disorders are a group of psychiatric conditions that can severely impact an individual’s ability to function in everyday environment, resulting in difficulties with daily activities, social connections, and behavioral stability (Ejima et al., 2024) . Conditions such as anxiety, addiction, depression, and bipolar disorder are common, with depression being a particularly pressing public health concern that demands effective treatment options (Kessler et al., 2007) . TCAs rank among the most commonly prescribed medications for depression, with over 25 million prescriptions written annually in the United States. However, despite their effectiveness, TCAs are frequently linked in overdose incidents, with studies showing that they contribute to nearly 25% of overdose-related hospital admissions at a major medical center (Marshall and Forker, 1982; Vandel et al., 1997) . According to the 2023 NSDUH Report, 22.8% of adults (58.7 million) experienced any mental illness (AMI) in the past year, and 4.5 million adolescents reported a major depressive episode, with 20% also experiencing substance use disorders. Suicide remains  \nFrontiers in Chemistry 01 [frontiersin.org](frontiersin.org)  \na major worry, with 5.0% of adults having serious thoughts about it, 1.4% making plans, and 0.6% attempting suici","cbCaij2bQ28upVmi","https://ap.wps.com/l/cbCaij2bQ28upVmi","pdf",2704148,1,14,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion\n# Keywords","[{\"question\":\"What is the main goal of the hydrogen-centric approach in this study?\",\"answer\":\"To use machine learning with a hydrogen representation to predict physicochemical properties of tricyclic antidepressant drugs via QSPR modeling based on topological indices.\"},{\"question\":\"How do the two molecular configurations differ?\",\"answer\":\"One configuration uses only explicit hydrogen atoms, while the other includes all hydrogen atoms in the molecular representation.\"},{\"question\":\"Which model performed better and what role did hydrogen representation play?\",\"answer\":\"Support vector regression (SVR) produced more accurate predictions than linear regression, and hydrogen representation influenced SVR’s predictions more strongly.\"}]","Hydrogen-centric machine learning approach for analyzing properties of tricyclic antidepressant drugs | 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is the main goal of the hydrogen-centric approach in this study?","Question",{"text":75,"@type":76},"To use machine learning with a hydrogen representation to predict physicochemical properties of tricyclic antidepressant drugs via QSPR modeling based on topological indices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the two molecular configurations differ?",{"text":80,"@type":76},"One configuration uses only explicit hydrogen atoms, while the other includes all hydrogen atoms in the molecular representation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed better and what role did hydrogen representation play?",{"text":84,"@type":76},"Support vector regression (SVR) produced more accurate predictions than linear regression, and hydrogen representation influenced SVR’s predictions more 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