The accurate prediction of binding free energies of drug molecules to protein targets using computational methods has become increasingly important in drug discovery applications. The GPU-accelerated AMBER TI code that comes as part of the AMBER software package, has been a popular and widely used tool for carrying out relative binding free energy (RBFE) calculations in drug discovery projects. Herein, we report the development and application of new GPU-accelerated free energy simulations methods in AMBER: 1) the new family of softcore potentials that overcome multiple well-known obstacles in AFE simulations, such as end-point catastrophes, particle collapses and large gradient-jump, which are commonly encountered in single-step alchemical free energy simulations, and 2) the new alchemical enhanced sampling (ACES) approach to accelerate convergence and obtain high precision ligand binding affinity predictions. We validate these new methods by calculating RBFEs on a series of openly available datasets and look critically at the importance of softcore potentials and enhanced sampling in RBFE simulations.

Validation of new GPU-accelerated free energy simulation methods in amber

Where thousands of chemistry professionals meet to share ideas and advance scientific and technical knowledge. Spring 2023 abstracts, posters, and presentations will launch at the end of March.

ACS Spring 2023

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.