{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T06:00:21Z","timestamp":1785218421055,"version":"3.55.0"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Breast cancer remains one of the leading causes of cancer-related mortality rate worldwide, and the identification of effective drug combinations is an essential requirement in pharmaceutical research. The integration of Artificial Intelligence (AI) in processing large volumes of chemical and biological data combines molecular representation, predictive modeling and structured support within a single accessible tool, which accelerates early-stage candidate identification for breast cancer research while promoting reproducibility, transparency and user centered design.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>The current research focuses on developing and designing \u201cPredictRx\u201d which is an artificial intelligence based driven decision support tool which tends to benefit healthcare practioners to analyze the combination of drug which can be utilized for breast cancer patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>PredictRx was developed using molecular descriptors, physicochemical properties, and drug interaction datasets collected from publicly available biomedical databases. The tool integrates in total six supervised and unsupervised learning techniques to examine the structural similarities between compounds and predict the potential drug interactions for breast cancer. Various machine learning techniques, including Random Forest, Support Vector Machine, Logistic Regression, K-Means Clustering, DBSCAN, and Agglomerative Clustering, to analyse structural similarities and predict potential drug interactions and synergy patterns. Model performance was evaluated using Classification matrix, Silhouette Score, Calinski-Harabasz Index, and Davies-Bouldin Index. The tool was deployed as a browser-accessible web application for real-time interaction and visualization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>The results suggests that Random Forest has the highest predictive performance accuracy of 1, and Agglomerative clustering delivered strongest scores (Silhouette Score: 0.6946; Davies-Bouldin Index: 0.2457). The current tool was deployed as a browser accessible web tool with possibility of real time interaction and result visualization. PredictRx is a distinctive easy to use, and interpretable screening tool focused on drug compatibility and synergy analysis. EDA further identified molecular weight, lipophilicity, and structural similarity as important contributors to drug compatibility prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>PredictRx shows how AI-driven predictive modeling which can speed up molecular screening and early-stage breast cancer medication discovery. The technology facilitates the effective identification of appropriate drug combinations and offers a scalable foundation for upcoming AI-assisted pharmaceutical research by combining clustering, classification, molecular representation, and visualization into a single interpretable platform.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1881187","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:37:31Z","timestamp":1785217051000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PredictRx: AI based decision support tool for molecular screening for breast cancer drug recommendation"],"prefix":"10.3389","volume":"9","author":[{"given":"Ritu","family":"Chauhan","sequence":"first","affiliation":[{"name":"Artificial Intelligence and IoT Lab, Centre for Computational Biology and Bioinformatics, Amity University","place":["Noida, India"]},{"name":"Malaysian Institute of Information Technology, Universiti Kuala Lumpur","place":["Kuala Lumpur, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neha","family":"Pandey","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and IoT Lab, Centre for Computational Biology and Bioinformatics, Amity University","place":["Noida, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megat F.","family":"Zuhairi","sequence":"additional","affiliation":[{"name":"Malaysian Institute of Information Technology, Universiti Kuala Lumpur","place":["Kuala Lumpur, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1021\/acscentsci.6b00367","article-title":"Low data drug recommendation with one-shot learning","volume":"3","author":"Altae-Tran","year":"2017","journal-title":"ACS Cent. 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