The advent of GPU computing has fueled the renewed interest in physics-based free energy simulations, namely free energy perturbation (FEP) and thermodynamic integration (TI), and their application in real-world drug discovery. Workflows to streamline the many steps involved in setting up and carrying out this type of calculations have been developed to better position these methods to be able to impact drug discovery in real-time. Success in applying these quantitative computational technologies to impact drug discovery has been recently reported and is consistent with our in-house experience. We aspire to apply Free Energy methods to all structurally enabled projects across our drug discovery portfolio, to drive decision making and to help us get to better molecules faster. In this presentation, we will highlight our results in benchmarking and validating different methods and software programs (e.g., Schrodinger FEP+, MOE/AMBER TI, OpenMM perses) and our strategy to provide a robust and easy to use solution at scale. Case studies representing prospective and retrospective applications will illustrate the impact of these cutting-edge prediction methods on drug discovery.

Running free energy simulations cheaper, faster and at scale to impact drug discovery

ACS Fall 2021

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