SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Drug Discovery

Background:

• Quantum mechanical (QM) methods are crucial for accurate protein-ligand interaction description in drug design.
• Standard QM methods face computational challenges due to unfavorable scaling.
• QM/MM, fragmentation, and semiempirical (SQM) methods offer computational solutions.

Purpose of the Study:

• To develop and validate a reliable SQM-based scoring function for structure-based drug design.
• To introduce and evaluate a faster SQM/COSMO scoring approach.
• To demonstrate the utility of SQM/COSMO in virtual screening and lead optimization.

Main Methods:

• Parametrization of SQM and implicit solvent models.
• Development of a novel SQM-based scoring function.
• Application and validation of the SQM/COSMO approach for pose identification and virtual screening.

Main Results:

• SQM-based approaches significantly improve docking accuracy and lead compound refinement.
• The developed SQM/COSMO scoring approach outperforms standard methods in native pose identification.
• SQM/COSMO demonstrated effectiveness in a proof-of-concept virtual screening enrichment study.

Conclusions:

• The SQM/COSMO approach provides a reliable and efficient tool for structure-based drug design.
• Its superior performance, feasibility, and chemical generality make it a valuable asset.
• This method enhances accuracy in protein-ligand interaction analysis and virtual screening.