  Most widely used ligand docking methods assume a rigid protein structure.
This leads to problems when the structure of the target protein deforms upon
ligand binding. In particular, the ligand's true binding pose is often scored
very unfavorably due to apparent clashes between ligand and protein atoms,
which lead to extremely high values of the calculated van der Waals energy
term. Traditionally, this problem has been addressed by explicitly searching
for receptor conformations to account for the flexibility of the receptor in
ligand binding. Here we present a deep learning model trained to take receptor
flexibility into account implicitly when predicting van der Waals energy. We
show that incorporating this machine-learned energy term into a
state-of-the-art physics-based scoring function improves small molecule ligand
pose prediction results in cases with substantial protein deformation, without
degrading performance in cases with minimal protein deformation. This work
demonstrates the feasibility of learning effects of protein flexibility on
ligand binding without explicitly modeling changes in protein structure.
