Machine Learning Approaches in Drug Discovery: Current Trends and Future Perspectives

Abstract

The integration of machine learning (ML) algorithms in pharmaceutical research has revolutionized the drug discovery process. This paper presents a comprehensive analysis of current ML methodologies employed in various stages of drug development, from target identification to clinical trial optimization. We review deep learning architectures for molecular property prediction, reinforcement learning for molecular generation, and graph neural networks for protein-ligand interaction modeling. Our analysis covers successful case studies including the discovery of novel antibiotics and COVID-19 therapeutics. The paper concludes with a discussion of remaining challenges, including data quality issues, interpretability concerns, and regulatory considerations for AI-discovered drugs.

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