A physics-aware neural network for protein–ligand interactions with quantum chemical accuracy
Zachary L. Glick, Derek P. Metcalf, Caroline S. Glick, Steven A. Spronk, Alexios Koutsoukas, Daniel L. Cheney, C. David Sherrill · Chemical Science · 2024
an intermediate prediction of monomer electron densities. The AP-Net model is trained on a comprehensive dataset composed of paired ligand and protein fragments. This model accurately predicts QC-quality interaction energies of protein-ligand systems at a computational cost reduced by orders of magnitude. Applications of the AP-Net model to molecular crystal structure prediction are explored, as well as limitations in modeling highly polarizable systems.