A Review on Artificial Intelligence and Machine Learning for Accelerated Drug Discovery and Development

Review Article

Abstract

Traditional drug discovery is a protracted and resource-intensive endeavor, often spanning over a decade with substantial financial requirements and high attrition rates in clinical phases. Artificial intelligence and machine learning algorithms offer a robust foundation for streamlining these processes by facilitating the analysis of vast chemical libraries and biological datasets. In target identification, deep learning architectures enable the prediction of protein-ligand interactions with high precision, while high-throughput screening data analysis identifies viable drug candidates more efficiently than conventional methods. Beyond early-stage discovery, computational models play a critical role in predicting Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiles, thereby mitigating the risk of late-stage failures. The emergence of personalized medicine further benefits from these technologies through the integration of genomic profiles and clinical histories to tailor therapeutic interventions for specific patient cohorts. However, significant barriers persist, including the requirement for high-quality, reproducible datasets and the substantial environmental footprint associated with training large-scale models. Overcoming these limitations through collaborative, multidisciplinary approaches is essential for the full realization of automated pharmaceutical development. The transition toward an AI-driven paradigm promises to enhance the efficacy, safety, and cost-effectiveness of novel therapeutics, ultimately improving global public health outcomes