Artificial Intelligence in Drug Discovery and Drug Repurposing: Recent Developments, Challenges, and Clinical Applications
KECERDASAN ARTIFICIAL DALAM DRUG DISCOVERY DAN DRUG REPURPOSING: PERKEMBANGAN TERKINI, TANTANGAN, DAN APLIKASI KLINIS
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Fauziah Hasdin
Universitas Negeri Makassar
Abstract
Drug development is a complex, time-consuming, and costly process with a high failure rate across preclinical and clinical stages. The rapid advancement of Artificial Intelligence (AI) has introduced innovative approaches to improve the efficiency and effectiveness of both drug discovery and drug repurposing by enabling the analysis of large-scale biological, chemical, and clinical datasets. This review aims to examine recent advances in AI applications for drug discovery and drug repurposing, evaluate their contributions across different stages of drug development, and discuss the challenges associated with their implementation in pharmaceutical sciences. This article was conducted as a narrative review based on literature retrieved from PubMed, Scopus, ScienceDirect, SpringerLink, Web of Science, and Google Scholar, covering publications from 2021 to 2026. Eligible studies were selected according to their relevance to AI, machine learning, deep learning, drug discovery, drug repurposing, medicinal chemistry, and clinical pharmacy. The reviewed literature demonstrates that AI plays a pivotal role in target identification, virtual screening, lead optimization, prediction of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties, and clinical trial optimization. Furthermore, AI accelerates drug repurposing through the integration of multi-omics data, knowledge graphs, and biological network analysis, enabling the efficient identification of novel therapeutic indications. Despite its considerable potential, the implementation of AI remains challenged by issues related to data quality, algorithm transparency, model validation, regulatory frameworks, and ethical considerations. Overall, AI has emerged as a transformative technology capable of accelerating drug development, improving decision-making, and supporting the advancement of precision medicine through multidisciplinary collaboration among pharmaceutical scientists, clinicians, computer scientists, and regulatory authorities.
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