Artificial intelligence: redefining the contours of drug discovery by transforming pharmaceutical research

Abstract

The integration of artificial intelligence (AI) into drug discovery and development is rapidly transforming
pharmaceutical research, offering unprecedented opportunities to streamline processes, reduce costs, and
accelerate timelines. The objectives of this review was to explore how artificial intelligence transforms drug
discovery by enhancing target identification, lead optimization, and clinical development while addressing
current challenges and future opportunities. Major scientific databases including PubMed, Scopus,
ScienceDirect, Web of Science, and Google Scholar were searched for articles published between 2010 and
2025. The search strategy involved combinations of keywords such as “artificial intelligence,” “machine
learning,” “deep learning,” “drug discovery,” “drug development,” “pharmaceutical research,” “clinical
trials,” and “computational drug design.” AI-driven technologies ranging from machine learning algorithms
to deep learning models are increasingly employed across various stages of the drug development pipeline,
including target identification, lead compound discovery, preclinical validation, and clinical trial
optimization. By leveraging large-scale biological, chemical, and clinical datasets, AI enables the
identification of novel drug candidates, prediction of drug-target interactions, and assessment of
pharmacokinetic and pharmacodynamic profiles with improved accuracy and efficiency. Additionally, AI
facilitates the repurposing of existing drugs and supports personalized medicine approaches by analyzing
patient-specific genomic and clinical data. Despite its promise, the adoption of AI in pharmaceutical
research faces challenges, such as data quality and standardization, algorithm transparency.