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

Abstract

The integration of machine learning (ML) algorithms in pharmaceutical research has revolutionised the drug-discovery process. This paper presents a comprehensive analysis of current ML methodologies employed at various stages of drug development, from target identification to clinical-trial optimisation. We review deep-learning architectures for molecular property prediction, reinforcement learning for molecular generation, and graph neural networks for protein-ligand interaction modelling. Our analysis covers successful case studies including the discovery of novel antibiotics and antiviral 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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