Template-Based Method for Conformation Generation and Scoring for Congeneric Series of Ligands.

Physics-based prediction of protein-ligand binding affinities for a congeneric series of ligands in lead optimization requires their geometries as a first step. In this paper, we report a method that uses the 3D conformation of a lead compound in complex with a protein as a template to generate conformations of a series of related analog compounds. The method uses the Maximal Common Substructure (MCS) computed between lead and analog ligands to assign coordinates for the atoms shared between the ligands. For the differing atoms, a conformation generation procedure is implemented that results in a diversity of conformations. The generated conformations are sorted using a score based on the Molecular Mechanics and Generalized Born with Solvent Accessible Surface Area contribution (MM-GBSA) method. The accuracy of the generated conformations is tested retrospectively using a cross-validation approach applied to four data sets obtained from the Drug Design Data Resource (D3R) by measuring the RMSD of the top scored conformation with respect to the crystallographic pose. The scoring ability of the method is independently assessed using data for the same protein targets to test the rank ordering ability and separating active and inactive ligands. We tested the effect of protein flexibility during structural optimization and scoring approaches with and without strain energies. Retrospective validation on data sets comprising 4 targets shows that the method outperforms random selection for all targets and outperforms a molecular weight-based null model in 3 out of 4 targets in separating active and inactive compounds. Therefore, the presented method is expected to be of utility in lead optimization for rapidly screening analog ligands and generating initial conformations for use in more detailed physics-based binding affinity prediction methods.