Automated Force Field Developer and Optimizer Platform: Torsion Reparameterization

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Abstract
Generic force fields such as Generalized Amber Force Field (GAFF) are widely used in protein-ligand binding simulations in structure-based drug discovery. However, the force field parameters are not always transferable across ligand molecules, and reparameterization is necessary for accurate binding free energy simulations. This is especially true for torsion parameters which are highly dependent on stereoelectronic and steric effects. Here we report a novel, flexible, and user-friendly computational tool called the Automated Force Field Developer and Optimizer (AFFDO) platform that allows generating accurate GAFF2 torsion parameters for drug-like molecules. For a given ligand, AFFDO selects the most important torsions, carries out GPU-accelerated density functional theory calculations to collect reference data and fits torsion terms using a fast gradient-based optimizer that leverages automated differentiation. We benchmark AFFDO by parameterizing a series of drug-like molecules and carrying out protein-ligand relative binding free energy (RBFE) simulations. The results show that our tool is capable of significantly improving GAFF2 torsion parameters and RBFE values within a reasonable amount of time.

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