Machine Learning Applications in Drug Discovery

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This review reveals ML's transformative impact on drug discovery, highlighting challenges and future directions.

Machine learning dramatically enhances efficiency in identifying new therapeutics in drug discovery, showing promise for the future.
Key methodologies include supervised learning, unsupervised learning, and reinforcement learning, impacting drug development stages.
Comprehensive analysis of case studies reveals how ML predicts drug-target interactions, optimizing drug design and trials.
Highlights the challenges of implementing machine learning while showcasing trends that may further advance drug discovery.

Ugurlu et al. (2025) studied this question.

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