Session

Description Drug discovery is a difficult process that often relies on fortuitous discoveries. The number of such discoveries has stagnated for decades despite numerous technological advances. The role of HPC in the field is undergoing transformations due to the advent of large-scale machine learning models in recent years, which promise to revolutionize parts of the discovery pipeline. Traditionally, computation provides ways to sample the mutual conformational space of ligands and receptors or predicts physicochemical properties of small molecules, to name just two. Our minisymposium zooms in on the following overarching considerations: first, a mix of access to technology, computational resources, and data dictates the ease and feasibility of use and therefore the widespread adoption of modern, AI-based prediction methods. Second, it is the particular challenge of complexes of small molecules and receptors that they pose many specific problems but offer little generalizability. Third, it is difficult to analyze results objectively when the ultimate goal is simply to discover a new binder, which has limited the ability to transfer and abstract knowledge. Following these lead concerns, our minisymposium is meant to foster the exchange of technologies and to fortify the ongoing discourse on objectivity and standardization in computational drug discovery.