{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T08:44:02Z","timestamp":1790153042902,"version":"4.0.1"},"reference-count":115,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR AI"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cisplatin resistance remains a significant obstacle in cancer therapy, frequently driven by translesion DNA synthesis mechanisms that use specialized polymerases such as human DNA polymerase \u03b7 (hpol \u03b7). Although small-molecule inhibitors such as PNR-7-02 have demonstrated potential in disrupting hpol \u03b7 activity, current compounds often lack sufficient potency and specificity to effectively combat chemoresistance. The vastness of chemical space further limits traditional drug discovery approaches, underscoring the need for advanced computational strategies such as machine learning (ML)\u2013enhanced quantitative structure-activity relationship (QSAR) modeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to develop and validate ML-augmented QSAR models to accurately predict hpol \u03b7 inhibition by indole thio-barbituric acid analogs, with the goal of accelerating the discovery of potent and selective inhibitors that could overcome cisplatin resistance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A curated library of 85 indole thio-barbituric acid analogs with validated hpol \u03b7 inhibition data was used, excluding outliers to ensure data integrity. Molecular descriptors spanning 1D to 4D were computed in MAESTRO, resulting in 220 features. In total, 17 ML algorithms, including random forest, extreme gradient boosting (XGBoost), and neural networks, were trained using 80% of the data for training and evaluated with 14 performance metrics. Robustness was ensured through hyperparameter optimization and 5-fold cross-validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Ensemble methods outperformed other algorithms, with random forest achieving near-perfect predictive performance (training mean square error=0.0002;\n                      <jats:italic>R<\/jats:italic>\n                      \u00b2=0.9999 and testing mean square error=0.0003;\n                      <jats:italic>R<\/jats:italic>\n                      \u00b2=0.9998). Shapley additive explanations analysis revealed that electronic properties, lipophilicity, and topological atomic distances were the most important predictors of hpol \u03b7 inhibition. Linear models exhibited higher error rates, highlighting the nonlinear relationship between molecular descriptors and inhibitory activity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Integrating ML with QSAR modeling provides a robust framework for optimizing hpol \u03b7 inhibition, offering both high predictive accuracy and biochemical interpretability. This approach accelerates the identification of potent selective inhibitors and represents a promising strategy for overcoming cisplatin resistance, thereby advancing precision oncology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/77890","type":"journal-article","created":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T17:00:08Z","timestamp":1755363608000},"page":"e77890-e77890","source":"Crossref","is-referenced-by-count":9,"title":["Machine Learning\u2013Enhanced Quantitative Structure-Activity Relationship Modeling for DNA Polymerase Inhibitor Discovery: Algorithm Development and 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[doi: 10.1007\/978-3-030-89010-0_9] ISBN: 9783030890100","DOI":"10.1007\/978-3-030-89010-0_9"},{"issue":"2","key":"R43","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","journal-title":"J R Stat Soc Series B"},{"issue":"2","key":"R44","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.jco.2009.01.002","article-title":"Elastic-net regularization in learning theory","volume":"25","author":"De Mol","journal-title":"J Complex"},{"issue":"3","key":"R45","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1109\/TIP.2017.2651396","article-title":"Discriminative elastic-net regularized linear regression","volume":"26","author":"Zhang","journal-title":"IEEE Trans Image Process"},{"key":"R46","doi-asserted-by":"crossref","unstructured":"Navada A Ansari AN Patil S Sonkamble BA . Overview of use of decision tree algorithms in machine learning. 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[doi: 10.1109\/ICCI51257.2020.9247843]","DOI":"10.1109\/ICCI51257.2020.9247843"},{"issue":"1","key":"R54","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1186\/s13321-023-00743-7","article-title":"Practical guidelines for the use of gradient boosting for molecular property prediction","volume":"15","author":"Boldini","journal-title":"J Cheminform"},{"key":"R55","doi-asserted-by":"crossref","unstructured":"Chen T Guestrin C . Xgboost: a scalable tree boosting system. In: Krishnapuram B Shah M , editors. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery; 2016:785-794. [doi: 10.1145\/2939672.2939785] ISBN: 9781450342322","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"R56","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-33525-0","article-title":"Detection of the chronic kidney disease using XGBoost classifier and explaining the influence of the attributes on the model using SHAP","volume":"13","author":"Raihan","journal-title":"Sci Rep"},{"issue":"3","key":"R57","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","author":"Bent\u00e9jac","journal-title":"Artif Intell Rev"},{"key":"R58","doi-asserted-by":"publisher","DOI":"10.1177\/11795468221133611","article-title":"XGBoost, a novel explainable AI technique, in the prediction of myocardial infarction: a UK biobank cohort study","volume":"16","author":"Moore","journal-title":"Clin Med Insights Cardiol"},{"key":"R59","doi-asserted-by":"crossref","unstructured":"Zhang Y Ni M Zhang C et al. Research and application of adaboost algorithm based on SVM. Presented at: 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC); May 24-26, 2019:662-666; Chongqing, China. [doi: 10.1109\/ITAIC.2019.8785556]","DOI":"10.1109\/ITAIC.2019.8785556"},{"key":"R60","doi-asserted-by":"publisher","DOI":"10.1016\/j.phpro.2012.03.160","article-title":"AdaBoost for feature selection, classification and its relation with SVM, a review","volume":"25","author":"Wang","journal-title":"Phys Procedia"},{"issue":"1","key":"R61","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","article-title":"CatBoost for big data: an interdisciplinary review","volume":"7","author":"Hancock","journal-title":"J Big Data"},{"issue":"11","key":"R62","doi-asserted-by":"publisher","first-page":"738","DOI":"10.14569\/IJACSA.2020.0111190","article-title":"Comparison of the CatBoost classifier with other machine learning methods","volume":"11","author":"Ibrahim","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"1","key":"R63","doi-asserted-by":"publisher","first-page":"6256","DOI":"10.1038\/s41598-022-10358-x","article-title":"Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction","volume":"12","author":"Uddin","journal-title":"Sci Rep"},{"issue":"1","key":"R64","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1186\/s40537-024-00973-y","article-title":"Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications","volume":"11","author":"Halder","journal-title":"J Big Data"},{"issue":"11","key":"R65","doi-asserted-by":"publisher","first-page":"218","DOI":"10.21037\/atm.2016.03.37","article-title":"Introduction to machine learning: k-nearest neighbors","volume":"4","author":"Zhang","journal-title":"Ann Transl Med"},{"key":"R66","doi-asserted-by":"crossref","unstructured":"Guo G Wang H Bell D Bi Y Greer K . KNN model-based approach in classification. In: Meersman R Tari Z Schmidt DC Meersman R Tari Z Meersman R Tari Z Schmidt DC , editors. On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. Springer; 2003:986-996. [doi: 10.1007\/978-3-540-39964-3_62] ISBN: 9783540399643","DOI":"10.1007\/978-3-540-39964-3_62"},{"key":"R67","doi-asserted-by":"crossref","unstructured":"Uhrig RE . Introduction to artificial neural networks. Presented at: IECON \u201995 - 21st Annual Conference on IEEE Industrial Electronics; Orlando, FL, USA. 1995.[doi: 10.1109\/IECON.1995.483329]","DOI":"10.1109\/IECON.1995.483329"},{"issue":"3","key":"R68","doi-asserted-by":"publisher","first-page":"83","DOI":"10.12779\/dnd.2018.17.3.83","article-title":"Artificial neural network: understanding the basic concepts without mathematics","volume":"17","author":"Han","journal-title":"Dement Neurocognitive Disord"},{"key":"R69","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: an overview","volume":"61","author":"Schmidhuber","journal-title":"Neural Netw"},{"issue":"12","key":"R70","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1097\/MEG.0b013e3282f198a0","article-title":"Introduction to artificial neural networks","volume":"19","author":"Grossi","journal-title":"Eur J Gastroenterol Hepatol"},{"issue":"3","key":"R71","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s41324-022-00494-x","article-title":"The role of artificial neural network and machine learning in utilizing spatial information","volume":"31","author":"Goel","journal-title":"Spat Inf Res"},{"issue":"16","key":"R72","doi-asserted-by":"publisher","first-page":"10073","DOI":"10.1021\/acs.chemrev.1c00022","article-title":"Gaussian process regression for materials and molecules","volume":"121","author":"Deringer","journal-title":"Chem Rev"},{"key":"R73","unstructured":"Ebden M . Gaussian processes: a quick introduction. 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