Integrating Quantum Mechanics into Protein-Ligand Docking: Toward Higher Accuracy and Reliability
Abstract
I introduce two new methods, QFVina and QFVinardo, for protein-ligand docking that leverage precomputed high-quality conformational libraries with QM-optimized geometries and ab initio DFT-D4-based conformational rankings and strain energies. These methods provide greater accuracy in docking-based virtual screening by addressing the inaccuracies in intramolecular relative energies of conformations, a critical component often misrepresented in flexible ligand docking calculations. I demonstrate that numerous force field-based methods widely used today exhibit substantial errors in conformational relative energies, and that it is unrealistic to expect better accuracy from the faster scoring functions typically employed in docking. Consistent with these findings, I show that traditional flexible ligand docking often produces geometries with significant strain energies and large deviations, with magnitudes comparable to the protein-ligand binding energies themselves and much larger than the differences we aim to estimate in docking hitlists. By using physically realistic ligand conformations with accurate strain energies in the scoring function, QFVina and QFVinardo produce markedly different docking results, even with the same docking parameters and scoring functions for protein-ligand interaction energies. I analyzed these differences in docking hitlists and selected protein-ligand interactions using three protein targets from COVID-19 research.
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