Minisymposium Presentation

Molecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses.We will describe the training and development of convolutional neural networks for protein-ligand scoring and how these deep learning models are integrated into the GNINA molecular docking open source software. We will describe the role high performance computing played in the training and optimzation of these networks and how high throughput docking can be performed at scale. Successful prospective evaluations of GNINA will be discussed, including recent top performance in the Critical Assessment of Computational Hit-Finding Experiments (CACHE).