[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125179-en":3,"doc-seo-125179-105":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},125179,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",7,"Healthcare","Editorial: Machine Learning Advancements in Pharmacology - Transforming Drug Discovery and Healthcare","Editorial focuses on how machine learning (ML) is reshaping pharmacology, drug discovery, and healthcare decision-making. The piece highlights that ML leverages large datasets and computing to reveal patterns, predict outcomes, and accelerate development workflows. It introduces a research topic featuring five studies spanning original research and a systematic review, covering applications such as graph neural networks for drug discovery, interpretable analysis of electrophysiological data, and structure-based inference for protein-ligand binding affinity to improve screening and mechanistic understanding.","Chapman University Digital Commons  \n\n| Pharmacy Faculty Articles and Research | School of Pharmacy |\n| --- | --- |\n| 3-7-2025\u003Cbr>Editorial: Machine Learning Advancements in Pharmacology: Transforming Drug Discovery and Healthcare\u003Cbr>Moom Rahman Roosan\u003Cbr>Ramgopal Mettu\u003Cbr>Follow this and additional works at: [https://digitalcommons.chapman.edu/pharmacy_articles](https://digitalcommons.chapman.edu/pharmacy_articles)[ ](https://digitalcommons.chapman.edu/pharmacy_articles) Part of the Artificial Intelligence and Robotics Commons, and the Other Pharmacy and Pharmaceutical Sciences Commons |  |\n\nEditorial: Machine Learning Advancements in Pharmacology: Transforming Drug Discovery and Healthcare  \nComments  \nThis article was originally published in Frontiers in Pharmacology, volume 16, in 2025. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3389/fphar.2025.1583486](10.3389/fphar.2025.1583486)  \nCreative Commons License  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nCopyright The authors  \nTYPE Editorial  \nPUBLISHED 07 March 2025  \nDOI 10.3389/fphar.2025.1583486  \nOPEN ACCESS  \nEDITED AND REVIEWED BY  \nHeike Wulff,  \nUniversity of California, Davis, United States  \n*CORRESPONDENCE  \nMoom Rahman Roosan,  [roosan@chapman.edu](roosan@chapman.edu)  \nRECEIVED 26 February 2025  \nACCEPTED 27 February 2025  \nPUBLISHED 07 March 2025  \nCITATION  \nRoosan MR and Mettu R (2025) Editorial: Machine learning advancements in pharmacology: transforming drug discovery and healthcare.  \nFront. Pharmacol. 16:1583486 .  \ndoi: 10.3389/fphar.2025.1583486  \nCOPYRIGHT  \n© 2025 Roosan and Mettu. 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.  \nEditorial: Machine learning advancements in pharmacology:  \ntransforming drug discovery and healthcare  \nMoom Rahman Roosan 1* and Ramgopal Mettu 2  \n1Pharmacy Practice Department, Chapman University School of Pharmacy, Irvine, CA, United States, 2Department of Computer Science, Tulane University, New Orleans, LA, United States  \nKEYWORDS  \nmachine learning (ML), artificial intelligence in healthcare, computational drug design and discovery, drug repurposing 4, predictive analytics in healthcare, survival prediction models  \nEditorial on the Research Topic  \nMachine learning advancements in pharmacology: transforming drug discovery and healthcare  \nIntroduction  \nIn recent years, the integration of machine learning (ML) into pharmacology has revolutionized how we approach drug discovery, disease modeling, and therapeutic development. By leveraging vast datasets and computational power, ML has enabled researchers to uncover patterns, predict outcomes, and accelerate drug development processes that were previously unimaginable. This Research Topic on “Machine Learning Advancements in Pharmacology” features five impactful studies that highlight the diverse applications and potential of ML in this field. These contributions, encompassing original research and a systematic review, exemplify the transformative role of ML in addressing some of the most pressing challenges in pharmacology.  \nDrug discovery  \nYao et al. conducted a bibliometric analysis of graph neural networks (GNNs) in drug discovery between 2017 and 2023 . Their findings reveal significant contributions from China and the United States in areas such as drug-target interaction prediction, drug repurposing, and drug-drug interaction analysis. While GNNs demonstrate promising applications, challenges such as data availability, ethical considerations, computational demands, and the need for interpretability remain barriers to wide","cbCaitO74R7c5fuy","https://ap.wps.com/l/cbCaitO74R7c5fuy","pdf",579711,1,5,"English","en",105,"# Introduction\n## Drug discovery\n## Structure-based inference and binding affinity prediction\n## Interpretable ML for electrophysiological data\n## Graph neural networks and bibliometric insights","[{\"question\":\"What is the main focus of this editorial?\",\"answer\":\"The editorial examines how machine learning advancements are transforming pharmacology, particularly drug discovery, disease modeling, and therapeutic development.\"},{\"question\":\"Which types of studies does the research topic include?\",\"answer\":\"The research topic highlights five impactful studies, including original research and a systematic review, showcasing diverse ML applications in pharmacology.\"},{\"question\":\"How do graph neural networks contribute to drug discovery in this editorial?\",\"answer\":\"They can support tasks such as drug-target interaction prediction, drug repurposing, and drug-drug interaction analysis, while also facing challenges related to data, ethics, computation, and interpretability.\"}]","Editorial: Machine Learning Advancements in Pharmacology - 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