Simulation-based methods of estimating protein-ligand binding free energies are now widely used in drug discovery, due to their relatively good accuracy. However, their usefulness is still limited by persistent errors in their predictions and their heavy computational demands. The quality of the potential functions used in the molecular simulations underlying these methods is a strong determinant of both their accuracy and their speed. In this seminar, I will report on our lab’s multipronged approach to advancing potential functions with improved balances of accuracy and speed. - With the Open Force Field Initiative (OpenFF), extension of the Open Force Field general force field to proteins so that small molecules, proteins, and covalently modified proteins are treated self-consistently. - Next-generation Generalized Born (nxGB) implicit solvation model parameterized to host-guest absolute binding free energy (ABFE) data. So far, the nxGB model gives consistent improvement in protein-ligand ABFE accuracy over baseline GB, sometimes rivaling explicit solvent results, with about 6x speedup. - Electronic polarizability model integrated with OpenFF and immediately applicable to most of drug-like chemical space, via typed polarizabilities, customized AM1/BCC-inspired partial charging method, and moderate computational cost through use of the direct approximation. - Automated sampling and optimization of force field typing schemes and parameters, a systematic approach which gives the user control of the level of parsimony vs proliferation of types and hence parameters. Preliminary results suggest that the potential surfaces of existing force fields can be accurately replicated with much fewer parameters.

Advancing the accuracy, efficiency, and parsimony of potential functions for molecular simulations in drug discovery

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ACS Spring 2023

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