A Comprehensive Study on AI in Drug Discovery

Abstract

 Artificial Intelligence (AI) has become a disruptive force in pharmaceutical sciences and forensic applications, primarily because of its associated enhancement in efficiency, accuracy, and cost reduction.
This review explains in detail the role of AI across different stages of drug discovery,from target identification to lead compound generation, optimization, preclinical validation, clinical trial design, and drug repurposing. It focuses on using machine learning, deep learning, and natural language processing to analyse complex chemical and biological datasets,predict molecular interactions, and identify novel drug candidates. It also discusses AI’s potential in solving global health crises such as pandemics with the help of rapid drug repurposing. Forensic science is revolutionized with AI-driven platforms in detecting, characterizing, and profiling illicit substances, counterfeit drugs, and toxicological contaminants. By leveraging advanced algorithms, forensic scientists can determine chemical compositions with accuracy, trace
their origins, and enhance the reproducibility of analyses. Such enhancement in forensic investigation strengthens criminal justice outcomes. While such progress has been made, significant challenges persist concerning data quality, model interpretability, and ethical considerations. The requirement felt by many for more transparency and collaborative frameworks is underscored by issues of biased datasets, regulatory compliance, and the “black-box” nature of AI models. The paper has stressed the need for interdisciplinary efforts in biology, chemistry, computer science, and policy-making to approach these challenges.

How to cite this article:
Arora A, Kumar A, Jora G, Jangir A, Mundotiya N.
A Comprehensive Study on AI in Drug Discovery.
Rec Trends Pharm Tech IndI 2025; 7(2): 01-05.