  Deep learning promises to dramatically improve scoring functions for
molecular docking, leading to substantial advances in binding pose prediction
and virtual screening. To train scoring functions-and to perform molecular
docking-one must generate a set of candidate ligand binding poses.
Unfortunately, the sampling protocols currently used to generate candidate
poses frequently fail to produce any poses close to the correct, experimentally
determined pose, unless information about the correct pose is provided. This
limits the accuracy of learned scoring functions and molecular docking. Here,
we describe two improved protocols for pose sampling: GLOW (auGmented sampLing
with sOftened vdW potential) and a novel technique named IVES (IteratiVe
Ensemble Sampling). Our benchmarking results demonstrate the effectiveness of
our methods in improving the likelihood of sampling accurate poses, especially
for binding pockets whose shape changes substantially when different ligands
bind. This improvement is observed across both experimentally determined and
AlphaFold-generated protein structures. Additionally, we present datasets of
candidate ligand poses generated using our methods for each of around 5,000
protein-ligand cross-docking pairs, for training and testing scoring functions.
To benefit the research community, we provide these cross-docking datasets and
an open-source Python implementation of GLOW and IVES at
https://github.com/drorlab/GLOW_IVES .
