Open Free Energy: An Open Source Ecosystem for Alchemistry

This are the slides given as a talk at the ACS Spring 2024 conference.

Abstract:

Alchemical free energy methods have found tremendous success over the last few decades, becoming key components of drug discovery pipelines. Despite many ongoing innovations in this area, with several methodological improvements published each year, it remains challenging to consistently run free energy campaigns using state-of-the-art tools and best practices. In many cases, doing so requires expert knowledge, and/or the use of expensive closed source software. The Open Free Energy project (https://openfree.energy/) was created with the aim of addressing these issues. A joint effort between several academic and industry partners, the project aims to create and maintain reproducible and extensible open source tools for running large-scale free energy campaigns.

In this contribution we will summarize the current status of the project and its ecosystem of modular permissively licensed open-source Python tools to set up, calculate, and analyze both relative and absolute free energies. We will outline how these various components are brought together in the OpenFE toolkit (https://github.com/OpenFreeEnergy/openfe/) to create a robust framework for carrying out complete free energy campaigns. Additionally, we will present progress on exploring optimal transformation networks and mappings for protein-ligand binding calculations on a large set of benchmark systems. Here, we compare the outcomes of various binding free energy network topologies accessible within the OpenFE toolkit such as star, minimum spanning, LOMAP, and HiMAP graphs. We discuss the shortcomings and advantages of these various network types, particularly focusing on the balance between computational cost and accuracy. Finally, we detail the next steps for the toolkit, including the addition of support for other molecular dynamics engines, complex multi-topology approaches to free energy estimates, and ongoing curation of community binding free energy datasets.