Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential

In this study, we systematically studied the energy distribution
of bioactive conformations of small molecular ligands in their conformational
ensembles using ANI-2X, a machine learning potential, in conjunction
with one of our recently developed geometry optimization algorithms,
known as a conjugate gradient with backtracking line search (CG-BS).
We first evaluated the combination of these methods (ANI-2X/CG-BS)
using two molecule sets. For the 231-molecule set, ab initio calculations
were performed at both the ωB97X/6-31G­(d) and B3LYP-D3BJ/DZVP
levels for accuracy comparison, while for the 8,992-molecule set,
ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level.
For each molecule in the two molecular sets, up to 10 conformations
were generated, which diminish the influence of individual outliers
on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS
in these evaluations, we calculated the energy distributions using
ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank
(PDB). Each ligand has at least one conformation bound to a biological
molecule, and this ligand conformation is labeled as a bound conformation.
Besides the bound conformations, up to 200 conformations were generated
using OpenEye’s Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each conformation. We performed a statistical analysis
of how the bound conformation energies are distributed in the ensembles
for 17,197 PDB ligands that have their bound conformation energies
within the energy ranges of the Omega2-generated conformation ensembles.
We found that half of the ligands have their relative conformation
energy lower than 2.91 kcal/mol for the bound conformations in comparison
with the global conformations, and about 90% of the bound conformations
are within 10 kcal/mol above the global conformation energies. This
information is useful to guide the construction of libraries for shape-based
virtual screening and to improve the docking algorithm to efficiently
sample bound conformations.