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 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 ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP level 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 diminishes 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 an “active” conformation. Besides the active conformations, up to 200 conformations were generated using OpenEye’s Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each ligand. We performed a statistical analysis on how the active conformational energies are distributed in the ensembles for 17,197 PDB ligands. We found that on average the active conformation energy is 2.91 kcal/mol higher than the global conformation energies. About 90% of the active 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 active conformations.

Dongxiao Hao

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