[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121201-en":3,"doc-seo-121201-105":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},121201,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","MACHINE LEARNING IN DRUG DISCOVERY - A CRITICAL REVIEW OF APPLICATIONS AND CHALLENGES","This review critically examines the integration of Machine Learning (ML) in drug discovery, highlighting its applications across target identification, hit discovery, lead optimization, and predictive toxicology. Despite ML's potential to revolutionize drug discovery through enhanced efficiency, predictive accuracy, and novel insights, significant challenges persist. These include issues related to data quality, model interpretability, integration into existing workflows, and regulatory and ethical considerations. The review advocates for advancements in algorithmic approaches, interdisciplinary collaboration, improved data-sharing practices, and evolving regulatory frameworks as potential solutions to these challenges.","Computer Science & IT Research Journal, Volume 5, Issue 4, April 2024  \nOPEN ACCESS  \nComputer Science & IT Research Journal P-ISSN: 2709-0043, E-ISSN: 2709-0051 Volume 5, Issue 4, P.892-902, April 2024  \nDOI: 10.51594/csitrj.v5i4.1048  \nFair East Publishers [Journal Homepage: ](Journal Homepage: www.fepbl.com/index.php/csitrj)[www.fepbl.com/index.php/csitrj](Journal Homepage: www.fepbl.com/index.php/csitrj)  \nMACHINE LEARNING IN DRUG DISCOVERY: A CRITICAL REVIEW OF APPLICATIONS AND CHALLENGES  \nFrancisca Chibugo Udegbe 1, Ogochukwu Roseline Ebulue2, Charles Chukwudalu Ebulue3, &  \nChukwunonso Sylvester Ekesiobi4  \n1Independent Researcher, Iowa, USA  \n2Nigerian Institute for Trypanosomiasis and Onchocerciasis Research (NITRA), Asaba, Nigeria 3Department of Community Medicine and Primary Healthcare, Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra State, Nigeria  \n4Department of Economics,  \nChukwuemeka Odumegwu Ojukwu University, Igbariam, Anambra State, Nigeria  \n\n| *Corresponding Author: Francisca Chibugo Udegbe\u003Cbr>[Corresponding Author Email: ](Corresponding Author Email: udegbefrancisca14@gmail.com)[udegbefrancisca14@gmail.com](Corresponding Author Email: udegbefrancisca14@gmail.com)\u003Cbr>Article Received: 10-01-24 Accepted: 15-03-24 Published: 17-04-24\u003Cbr>Licensing Details: Author retains the right of this article. The article is distributed under the terms of the\u003Cbr>Creative Commons Attribution-NonCommercial 4.0 License\u003Cbr>([http://www.creativecommons.org/licences/by-nc/4.0/](http://www.creativecommons.org/licences/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the Journal open access page |\n| --- |\n| ABSTRACT\u003Cbr>This review critically examines the integration of Machine Learning (ML) in drug discovery, highlighting its applications across target identification, hit discovery, lead optimization, and predictive toxicology. Despite ML's potential to revolutionize drug discovery through enhanced efficiency, predictive accuracy, and novel insights, significant challenges persist. These include issues related to data quality, model interpretability, integration into existing workflows, and regulatory and ethical considerations. The review advocates for advancements in algorithmic approaches, interdisciplinary collaboration, improved data-sharing practices, and evolving regulatory frameworks as potential solutions to these challenges. By addressing these hurdles and |\n\nUdegbe, Ebulue, Ebulue, & Ekesiobi, P. 892-902 Page 892  \nleveraging the capabilities of ML, the drug discovery process can be significantly accelerated, paving the way for the development of new therapeutics. This review calls for continued research, collaboration, and dialogue among stakeholders to realize the transformative potential of ML in drug discovery fully.  \nKeywords: Machine Learning, Drug Discovery, Predictive Toxicology, Data Quality, Interdisciplinary Collaboration.  \nINTRODUCTION  \nThe quest for new pharmaceuticals is a complex, costly, and time-consuming endeavour integral to advancing medical science and enhancing human health. The traditional drug discovery process, from target identification to clinical trials and regulatory approval, can span over a decade and cost upwards of a billion dollars per drug (Berdigaliyev & Aljofan, 2020; Rudrapal, Khairnar, & Jadhav, 2020) . This process begins with identifying a biological target associated with a disease. It proceeds through designing, synthesizing, and testing compounds that affect the target's activity. Despite technological advancements, the rate of bringing new, effective drugs to market has not significantly increased, highlighting a critical need for innovation (Kiriiri, Njogu, & Mwangi, 2020; Park, Otte, & Park, 2022; Vargason, Anselmo, & Mitragotri, 2021) .  \nMachine Learning (ML) presents a transformative potential for drug discovery. It aims to reshape","cbCainsdmGUS1pWI","https://ap.wps.com/l/cbCainsdmGUS1pWI","pdf",181334,1,11,"English","en",105,"# Introduction\n## Applications of Machine Learning in Drug Discovery\n## Potential Impact on Precision Medicine\n## Purpose and Scope of the Review","[{\"question\":\"Which stages of drug discovery does the review cover for machine learning applications?\",\"answer\":\"It covers target identification, hit discovery, lead optimization, and predictive toxicology.\"},{\"question\":\"What challenges are highlighted when integrating machine learning into drug discovery?\",\"answer\":\"Key challenges include data quality, model interpretability, workflow integration, and regulatory and ethical considerations.\"},{\"question\":\"How does machine learning support improved drug discovery outcomes according to the review?\",\"answer\":\"It can enhance efficiency and predictive accuracy, uncover hidden patterns, and provide novel biological insights that accelerate development of new therapeutics.\"}]","MACHINE LEARNING IN DRUG DISCOVERY - 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