# Automated Force Field Developer and Optimizer Platform: Torsion Reparameterization

## TL;DR

This paper introduces a new tool called AFFDO that improves the accuracy of molecular simulations by optimizing torsion parameters for drug-like molecules.

## Contribution

The novel AFFDO platform automates torsion parameter reparameterization using GPU-accelerated DFT and gradient-based optimization.

## Key findings

- AFFDO significantly improves GAFF2 torsion parameters against quantum mechanical reference data.
- Optimized parameters lead to better agreement with experimental relative binding free energy values.
- The platform enables efficient and accurate drug-like molecule simulations.

## Abstract

General force fields
such as General Amber Force Field (GAFF) have
been designed for broad applicability and 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 custom reparameterization is sometimes 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, tailored 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 parametrizing a series of drug-like molecules
and carrying out protein–ligand relative binding free energy
(RBFE) simulations. The results show that AFFDO can significantly
improve GAFF2 torsion parameters against QM reference data, which
in some cases translates into better agreement with experimental RBFE
values within a reasonable computational time.