Drug discovery has long depended on slow, trial-and-error experimentation. Today, computational approaches—including AI and machine learning—are revolutionizing the field by enabling large-scale simulations to inform decision-making. These tools allow researchers to explore a vastly expanded set of potential interactions and outcomes at atomistic, DNA, protein, and cellular levels, thereby significantly improving the precision of design drugs for the right cellular and disease context and the right patient. 

This symposium will unite experts applying computational methods across drug discovery, including compound and protein design, protein structure and interaction prediction, disease modeling, and predictive toxicology. Through cross-disciplinary talks, speakers will demonstrate how advanced computation and machine learning are driving hypothesis generation, accelerating therapeutic innovation, and transforming the future of medicine.