Machine Learning Approaches for Drug Discovery and Development

Machine learning algorithms are transforming the pharmaceutical industry by accelerating drug discovery and reducing development costs. This study presents a comprehensive analysis of various ML approaches applied to target identification, lead optimization, and toxicity prediction. We evaluate the performance of deep learning models, random forests, and graph neural networks across multiple drug discovery tasks. Our benchmarking results demonstrate that ensemble methods combining multiple algorithms achieve superior prediction accuracy for drug-target interactions.