Machine Learning in Modern Drug Discovery: Applications, Advances, and Future Horizons

Abstract

The integration of machine learning (ML) into drug discovery is reshaping pharmaceutical development, addressing high costs exceeding $2 billion per drug, decade-long timelines, and success rates below 10%. This review categorizes ML applications into four domains: (1) Target Identification and Validation, where ML algorithms mine genomic and proteomic data to uncover novel therapeutic targets and validate their relevance; (2) Molecular Design and Screening, utilizing deep learning to design and optimize lead compounds, significantly reducing physical screening; (3) Predictive Toxicology and Pharmacokinetics, leveraging ensemble learning to predict safety profiles and ADMET properties with precision; and (4) Clinical Trial Optimization, employing patient stratification and real-time monitoring to streamline trials. Although ML offers transformative potential, challenges persist, including data quality inconsistencies, model interpretability, and regulatory hurdles. Emerging technologies like quantum computing, with its unparalleled molecular simulation capabilities, and generative AI, which excels in creating novel molecular entities, show promise in overcoming these obstacles. Drawing on recent advances, this review emphasizes the necessity of interdisciplinary collaboration among computational scientists, pharmacologists, and clinicians. Future directions include the adoption of federated learning for secure multi-institutional collaborations, explainable AI for greater transparency, and hybrid classical-quantum algorithms. Together, these innovations position ML as the cornerstone of next-generation drug discovery, accelerating timelines, reducing costs, and improving success rates.