{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T16:30:57Z","timestamp":1790094657596,"version":"4.0.1"},"reference-count":84,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"US Dept. of Veterans Affairs Research Career Scientist (RCS)","award":["IK6BX004212"],"award-info":[{"award-number":["IK6BX004212"]}]},{"name":"US Dept. of Veterans Affairs Research Career Scientist (RCS)","award":["IK6BX006032"],"award-info":[{"award-number":["IK6BX006032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and efficiency, thereby limiting success. This review explores how recent developments in machine learning (ML) are significantly impacting target-based drug discovery, particularly in small-molecule approaches. The Simplified Molecular Input Line Entry System (SMILES), which translates a chemical compound\u2019s three-dimensional structure into a string of symbols, is now widely used in drug design, mining, and repurposing. Utilizing ML and natural language processing techniques, SMILES has revolutionized lead identification, high-throughput screening and virtual screening. ML models enhance the accuracy of predicting binding affinity and selectivity, reducing the need for extensive experimental screening. Additionally, deep learning, with its strengths in analyzing spatial and sequential data through convolutional neural networks (CNNs) and recurrent neural networks (RNNs), shows promise for virtual screening, target identification, and de novo drug design. Fragment-based approaches also benefit from ML algorithms and techniques like generative adversarial networks (GANs), which predict fragment properties and binding affinities, aiding in hit selection and design optimization. Structure-based drug design, which relies on high-resolution protein structures, leverages ML models for accurate predictions of binding interactions. While challenges such as interpretability and data quality remain, ML\u2019s transformative impact accelerates target-based drug discovery, increasing efficiency and innovation. Its potential to deliver new and improved treatments for various diseases is significant.<\/jats:p>","DOI":"10.3390\/ijms252212233","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T08:06:32Z","timestamp":1731571592000},"page":"12233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4482-2206","authenticated-orcid":false,"given":"Akshata Yashwant","family":"Patne","sequence":"first","affiliation":[{"name":"Center for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA"},{"name":"Taneja College of Pharmacy Graduate Programs, MDC30, 12908 USF Health Drive, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sai Madhav","family":"Dhulipala","sequence":"additional","affiliation":[{"name":"Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Lawless","sequence":"additional","affiliation":[{"name":"Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA"},{"name":"Research Service, James A. Haley Veterans Hospital, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satya","family":"Prakash","sequence":"additional","affiliation":[{"name":"Biomedical Technology and Cell Therapy Research Laboratory, Department of Biomedical Engineering, Faculty of Medicine and Health Sciences, McGill University, 3775 University Street, Montreal, QC H3A 2B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5838-0681","authenticated-orcid":false,"given":"Shyam S.","family":"Mohapatra","sequence":"additional","affiliation":[{"name":"Center for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA"},{"name":"Taneja College of Pharmacy Graduate Programs, MDC30, 12908 USF Health Drive, Tampa, FL 33612, USA"},{"name":"Research Service, James A. Haley Veterans Hospital, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7636-0174","authenticated-orcid":false,"given":"Subhra","family":"Mohapatra","sequence":"additional","affiliation":[{"name":"Center for Research and Education in Nanobioengineering, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA"},{"name":"Taneja College of Pharmacy Graduate Programs, MDC30, 12908 USF Health Drive, Tampa, FL 33612, USA"},{"name":"Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA"},{"name":"Research Service, James A. Haley Veterans Hospital, Tampa, FL 33612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"ref_1","unstructured":"Foote, K.D. (Dataversity, 2019). 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