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

Area of Science:

• Computational chemistry
• Drug discovery
• Structural biology

Background:

• Pharmacophore models represent essential molecular features for target interaction.
• Consensus pharmacophores improve predictive power by integrating data from multiple ligands.
• Challenges exist in generating robust pharmacophores from large, diverse ligand sets.

Purpose of the Study:

• To present a protocol for constructing consensus pharmacophores using the ConPhar tool.
• To demonstrate the application of this protocol for virtual screening.
• To identify novel ligands for the SARS-CoV-2 main protease (Mpro).

Main Methods:

• Utilized ConPhar, an open-source informatics tool, for pharmacophore feature identification and clustering.
• Developed a protocol for pharmacophore model generation and refinement.
• Applied the protocol to 100 non-covalent Mpro inhibitors co-crystallized with the target.

Main Results:

• Generated a consensus pharmacophore model for SARS-CoV-2 Mpro.
• The model accurately captured key interactions in the Mpro catalytic region.
• Successfully identified potential new ligands through virtual screening.

Conclusions:

• The ConPhar-based protocol provides a robust method for generating consensus pharmacophores.
• This strategy is broadly applicable to various biological targets with available ligand-bound data.
• The approach supports rational drug discovery and streamlines the identification of novel drug candidates.