Exploring the Synergy of Machine Learning and Physics-Based Computational Chemistry to Accelerate Drug Discovery

Recent advancements in both Machine Learning (ML) and physics-based computational chemistry and their combination hold great promise in opening new avenues for faster, more efficient drug design. The 2025 GRC CADD will explore new computational approaches across both ML and physics-based computational chemistry that can enhance our understanding of molecular interactions, drive the accumulation of experimental data, and accelerate the design of novel molecules.

New modalities are increasingly becoming mainstream as the field looks to drug biological complex targets with strong biological rationales. The conference will include a broad view of computational research and approaches, focusing both on methodology and application, in pursuit of molecular design and experimentation. The conference will discuss advancements in the use of computation that leverages both physics-based and ML strategies, as well as applications that interface with key experimental techniques and newer modalities. The conference prioritizes the presentation of cutting-edge, unpublished, research while creating an open climate for in-depth discussion and informal interaction between diverse scientists at all career stages. We encourage all conference applicants to consider submitting a poster abstract for consideration.