{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T20:49:11Z","timestamp":1768423751120,"version":"3.49.0"},"reference-count":81,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AI"],"abstract":"<jats:p>The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10\u201320 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of reshaping the entire pharmaceutical research and development (R&amp;D) pipeline. The purpose of this narrative review is to examine the role of AI in drug discovery and development, highlighting its contributions, challenges, and future implications for pharmaceutical sciences and global public health. A comprehensive review of the scientific literature was conducted, focusing on published studies, reviews, and reports addressing the application of AI across the stages of drug discovery, preclinical development, clinical trials, and post-marketing surveillance. Key themes were identified, including AI-driven target identification, molecular screening, de novo drug design, predictive toxicity modelling, and clinical monitoring. The reviewed evidence indicates that AI has significantly accelerated drug discovery and development by reducing timeframes, costs, and failure rates. AI-based approaches have enhanced the efficiency of target identification, optimized lead compound selection, improved safety predictions, and supported adaptive clinical trial designs. Collectively, these advances position AI as a catalyst for innovation, particularly in promoting accessible, efficient, and sustainable healthcare solutions. However, substantial challenges remain, including reliance on high-quality and representative biomedical data, limited algorithmic transparency, high implementation costs, regulatory uncertainty, and ethical and legal concerns related to data privacy, bias, and equitable access. In conclusion, AI represents a paradigm shift in pharmaceutical research and drug development, offering unprecedented opportunities to improve efficiency and innovation. Addressing its technical, ethical, and regulatory limitations will be essential to fully realize its potential as a sustainable and globally impactful tool for therapeutic innovation.<\/jats:p>","DOI":"10.3390\/ai7010026","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T11:01:14Z","timestamp":1768388474000},"page":"26","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Algorithm to Medicine: AI in the Discovery and Development of New Drugs"],"prefix":"10.3390","volume":"7","author":[{"given":"Ana Beatriz","family":"Lopes","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Sciences, University Institute of Health Sciences\u2014CESPU (IUCS-CESPU), 4585-116 Gandra PRD, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8633-2230","authenticated-orcid":false,"given":"C\u00e9lia Fortuna","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Associate Laboratory i4HB\u2014Institute for Health and Bioeconomy, University Institute of Health Sciences\u2014CESPU (IUCS-CESPU), 4585-116 Gandra PRD, Portugal"},{"name":"UCIBIO\u2014Applied Molecular Biosciences Unit, Translational Toxicology Research Laboratory, University Institute of Health Sciences (1H-TOXRUN, IUCS-CESPU), 4585-116 Gandra PRD, Portugal"},{"name":"LEPABE\u2014Laboratory for Process Engineering, Environment, Biotechnology and Energy, ALiCE\u2014Associate Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5960-3610","authenticated-orcid":false,"given":"Francisco A. M.","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences, University Institute of Health Sciences\u2014CESPU (IUCS-CESPU), 4585-116 Gandra PRD, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"ref_1","first-page":"19","article-title":"Artificial Intelligence in Drug Development\u2014Revolutionizing Drug Discovery and Clinical Trials","volume":"8","author":"Banerjee","year":"2024","journal-title":"Acta Sci. Pharm. 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