Drug Discovery Using Machine Learning

Abstract

The process of drug discovery is inherently complex, time-consuming, and resource-intensive, requiring the identification, validation, and optimization of potential drug candidates. Traditional methods often suffer from inefficiencies related to cost, duration, and predictive limitations. Recent advancements in Machine Learning (ML) have transformed drug discovery by leveraging data-driven insights, automating key processes, and improving predictive accuracy. Various ML techniques, including supervised learning, deep learning, reinforcement learning, and natural language processing, have demonstrated substantial potential in critical areas such as virtual screening, drug-target interaction prediction, biomarker identification, and drug formulation optimization. These AIdriven approaches enable the identification of novel drug candidates, accelerate lead optimization, and enhance the efficiency of clinical trial design. Despite these advancements, challenges such as data quality, model interpretability, and generalization across diverse biological systems persist. This review examines the current landscape of ML in drug discovery, discusses key methodologies and applications, highlights existing challenges, and explores future directions to strengthen the role of ML in pharmaceutical research and development