Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro.

Abstract

A pharmacophore defines the spatial arrangement of molecular features required for optimal interactions between a compound and its biological target. These models can be derived by analyzing the intermolecular interactions between a target and a set of known ligands in their binding conformations. A consensus pharmacophore integrates common features from multiple ligands, reducing model bias and enhancing predictive power. However, generating a robust consensus pharmacophore from a large and chemically diverse ligand set presents technical challenges. Here, we present a protocol for the construction of consensus pharmacophores using ConPhar, an open-source informatics tool designed to identify and cluster pharmacophoric features across multiple ligand-bound complexes. The protocol includes model generation, refinement, and application to the virtual screening of ultra-large molecular libraries. As a case study, we applied the method to the SARS-CoV-2 main protease (Mpro), using one hundred non-covalent inhibitors co-crystallized with the target. The resulting pharmacophore model captured key interaction features in the catalytic region of Mpro and enabled the identification of new potential ligands. This strategy is broadly applicable to any biological target for which ligand-bound conformations are available. It is particularly valuable for targets with extensive ligand datasets and supports rational drug discovery by streamlining the identification of novel candidates with desired interaction profiles.