Artificial intelligence, computational tools and robotics for drug discovery, development, and delivery

Oyejide, AJ, Adekunle, YA, Abodunrin, OD and Atoyebi, EO (2025) Artificial intelligence, computational tools and robotics for drug discovery, development, and delivery. Intelligent Pharmacy, 3 (3). pp. 207-224. ISSN 2949-866X

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Abstract

The integration of Artificial Intelligence (AI) and robotics into the pharmaceutical sector is rapidly transforming drug discovery, development, and delivery (D-DDD) processes. Traditional drug development is often characterized by lengthy timelines, high costs, and complex challenges associated with target identification, drug efficacy, and safety profiling. AI and robotics offer transformative solutions, bringing speed, precision, and scalability to various stages of D-DDD. In this review, we analyze cutting-edge advancements in AI-driven predictive modeling, machine learning algorithms for molecular screening, and data mining techniques that enable efficient drug target identification and toxicity prediction. We also explore robotics applications that enhance automation in high-throughput screening, compound synthesis, and patient-specific drug delivery systems. Through examining the applications, limitations, and future trends of these technologies, this review provides a comprehensive outlook on the potential of AI and robotics to streamline the drug pipeline and enable personalized therapeutic strategies. Our review reveals that the convergence of AI, robotics, and big data has potential to reshape pharmaceutical research, reduce costs, and pave the way for more accessible, effective therapies. This review thus serves as a critical resource for understanding the future trajectory of intelligent, technology-driven pharmacy and its implications for advancing healthcare.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences; 3404 Medicinal and Biomolecular Chemistry; 34 Chemical Sciences; Rare Diseases; Data Science; Biotechnology; Bioengineering; Orphan Drug; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; 5.1 Pharmaceuticals; Generic health relevance; 3 Good Health and Well Being
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RS Pharmacy and materia medica
Divisions:	Pharmacy and Biomolecular Sciences
Publisher:	Elsevier
Date of acceptance:	17 January 2025
Date of first compliant Open Access:	9 July 2025
Date Deposited:	09 Jul 2025 09:39
Last Modified:	09 Jul 2025 09:45
DOI or ID number:	10.1016/j.ipha.2025.01.001
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26742

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