Pharmaqube journal
Review Article - (2025) Volume 1, Issue 1
The AI Revolution in Drug Discovery: A Comprehensive Review of Technologies, Triumphs, and Tribulations
#Equally contribution
Received Date: Sep 15, 2025 / Accepted Date: Oct 07, 2025 / Published Date: Oct 21, 2025
Abstract
Artificial intelligence (AI) and Machine learning (ML) are catalyzing a paradigm shift in addressing the persistent challenges of traditional drug discovery, a process long characterized by exorbitant costs, protracted timelines, and profoundly low success rates. This comprehensive review critically analyzes recent advancements in AI and ML methodologies across the entire pharmaceutical research and development (R&D) pipeline, from initial target identification through to clinical development. The analysis examines a diverse array of AI techniques, including foundational machine learning, deep learning architectures, graph neural networks, and the latest generation of generative and multimodal models. It details their application in crucial areas such as novel target discovery, de novo molecular design, hit-to-lead optimization, and preclinical safety assessment. A comparative analysis highlights the relative advantages, inherent limitations, and practical implementation challenges associated with these varied AI approaches, emphasizing the critical importance of data quality, model validation, and interpretability, and the complex web of regulatory and ethical considerations. This review synthesizes current applications and successes, identifies persistent gaps—particularly in data accessibility, clinical translation, and navigating the hype cycle—and proposes future directions to unlock the full potential of AI in creating safer, more effective, and accessible medicines. By emphasizing transparent methodologies, robust validation frameworks, and responsible governance, this report aims to guide the impactful and ethical integration of artificial intelligence into the future of pharmaceutical innovation.
Keywords
Artificial Intelligence, Machine learning, Drug Discovery, Deep Learning, Target Identification, De Novo Drug Design, Virtual Screening, Predictive Modelling, Clinical Trials, Pharmaceutical Research.
Citation: Rajaputana, L.M., Vinod, R., Bhavani, B., Nandabaram, A., Sudhakar, M., et al (2025). The AI Revolution in Drug Discovery: A Comprehensive Review of Technologies, Triumphs, and Tribulations. J pharm qube, 1(2), 1-21.
Copyright: © 2025 Rajaputana, L.M et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.