Open Free Energy’s software is open source

Today, Open Free Energy (OpenFE) announced the publication of its industry benchmarking results, indicating that OpenFE tooling provides accurate, high throughput binding affinity predictions at a quality that will have real-world impact on drug discovery campaigns across academia and industry. Based at a nonprofit, OpenFE is funded by a consortium and develops open-source software for free energy calculations to support drug discovery research.

Today’s publication, in the Journal of Chemical Information and Modeling https://pubs.acs.org/doi/10.1021/acs.jcim.6c00089, gives the results of 15 pharmaceutical companies testing OpenFE’s methods on over 1700 drug-like molecules, in both public datasets and internal, proprietary datasets in blind tests. These latter tests give a clear indication of the real-world accuracy of OpenFE in a drug discovery setting, and provide compelling evidence that OpenFE’s tooling is ready for deployment.

Pharmaceutical industry testing on these proprietary sets provides a particularly critical and real-world indication of performance, since these sets haven’t been released to the public and are representative of real-world, often live, drug discovery projects these companies are engaged in. Often, these internal proprietary sets involve targets or chemistries the companies have found particularly challenging and where they especially need guidance from computational methods, making OpenFE’s performance on these datasets particularly impressive.

“We benchmarked OpenFE using curated internal data and found its performance consistent with results reported in the paper. These findings, combined with its open-source license, suggest OpenFE can be a valuable addition to our structure-based drug design toolbox. We are currently exploring ways to deploy OpenFE at scale and integrate it into our computational workflows,” said BinQing Wei, a Senior Principal Scientist in Discovery Chemistry at Genentech.

This study also warrants particular note given the field’s current interest in machine learning and co-folding models for predicting binding. While such methods often perform well on public test sets, so far their performance on new chemistries and proprietary datasets has typically been far worse. In contrast, OpenFE’s performance reported in this study on proprietary datasets is quite good – making these methods especially appealing for pharmaceutical applications.

OpenFE achievements are noteworthy as the result of a precompetitive industry funded consortium’s work to provide vital infrastructure for early stage drug discovery. OpenFE software is released under open source licenses, so it can be used in large-scale drug discovery campaigns without incurring licensing fees. This allows companies to scale their projects to any level, while only paying the cost of compute while using OpenFE’s tooling. The software’s open source licensing allows companies to easily build on OpenFE, extend it, and integrate it into their workflows with confidence. For more, see the project’s website at https://openfree.energy. Instructions on how to install and run the software can be found in the project’s documentation (https://docs.openfree.energy/en/latest/). Open Free Energy is also seeking new industry partners, as described below.

About Open Free Energy

Open Free Energy is consortium hosted by the Open Molecular Software Foundation, a non-profit organization that brings together academic researchers, industry users, and open source developers to move software tools from the lab into industrial use. OMSF’s expertise, and the consortium structure, ensure that the tools developed meet real-world needs and conform to best practices. The software is developed by a professional team of software engineers and scientists, funded and directed by a pre-competitive consortium of partner companies in the pharmaceutical, biotech, and software industries.

OpenFE sits at the nexus of academia and industry. Its software is designed to be stable, reliable, and thoroughly tested, as required by industry, while also providing tools that make it easier for academics to develop the new methods that will become the next industry standards. It builds bridges that enable both academic and industrial users to benefit from the latest methods being developed by free energy experts.

Companies interested in joining the consortium, accelerating the development of new features, and further taking advantage of the latest science in this area, should contact project manager James Eastwood (james.eastwood@omsf.io) and project director David Mobley (david.mobley@omsf.io).

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