Members
Democratizing AI for Biology
An open-source project that can be used and improved by academics and companies alike.
Utilize the existing pre-trained weights to get quickly get started fine-tuning your model.
A permissively licensed model that allows commercial and non-commercial use.
Provides the tools used to train the model under the same license.
Optimized performance for use on state-of-the-art and widely available GPUs.
A supercomputer scale, distributed training, PyTorch-based training framework
OpenFold is a non-profit AI research and development consortium developing free and open-source software tools for biology and drug discovery. Our mission is to bring the most powerful software ever created -- AI systems with the ability to engineer the molecules of life -- to everyone. These tools can be used by academics, biotech and pharmaceutical companies, or students learning to create the medicines of tomorrow, to accelerate basic biological research, and bring new cures to market that would be impossible to discover without AI.
In biology, structure and function are inextricably linked. Understanding the mechanisms of biological systems, their engineering, and how to affect them therefore implies a need to know and understand their structure. The consortium is creating state-of-the-art AI-based protein modeling tools that can predict molecular structures with atomic accuracy, making this level of precision accessible in open source for both research and commercial applications for the first time. Researchers around the world will be able to use, improve, and contribute to this "predictive molecular microscope.”
This work aims to:
OpenFold, a non-profit artificial intelligence (AI) research consortium, today announced the release of two new tools: 1) SoloSeq, which integrates a new protein Large Language Model (LLM) with its OpenFold structure prediction software, and 2) OpenFold-Multimer software, which creates higher quality models of protein/protein complexes than OpenFold alone.
The OpenFold group, a non-profit artificial intelligence (AI) research consortium of biotech and tech firms whose goal is to develop free and open-source software tools for biology and drug discovery, is announcing the funding of new large-scale protein studies at Prof. Gabriel Rocklin’s laboratory at Northwestern University. Read the full story in Business Wire.
OpenFold, a non-profit artificial intelligence (AI) research consortium whose goal is to develop free and open-source software tools for biology and drug discovery, today announced the addition of three new industry members: UCB, NVIDIA, and Valence Labs (powered by Recursion). Read the full story in Business Wire.
OpenFold, a non-profit artificial intelligence (AI) research consortium whose goal is to develop free and open source software tools for biology and drug discovery, today announced the addition of four new industry members: Bayer, Dassault Systèmes, CHARM Therapeutics, and BaseCamp Research Ltd. Read the full story in Business Wire.
A set of leading academic and industry partners are announcing the formation of OpenFold, a non-profit artificial intelligence (AI) research consortium of organizations whose goal is to develop free and open source software tools for biology and drug discovery. OpenFold is a project of the Open Molecular Software Foundation (OMSF), a non-profit organization advancing molecular sciences by building communities for open source research software development. Read the full story in Business Wire.
Gustaf Ahdritz is the PhD student in the Al Quraishi lab at Columbia University and one of the lead developers of OpenFold. He shares his thoughts in this TechCrunch piece on the value of open source and community in science, AI models and why OpenFold came about when AlphaFold2 is already out there.
All entities are welcome to join the consortium -- corporate, non-profit, and academic. Members contribute financially or technically and play a key role in choosing new directions and high-priority projects. Please note that joining the consortium as a member is primarily directed at organizations and requires committing resources through a participation agreement. If you are interested in becoming a member, please fill out the form linked below and we will follow up with more details.
Join the ConsortiumThe Consortium also benefits from support provided by different non-member organizations and individuals with aligned missions -- if you have a research idea or seek other collaboration opportunities, please reach out by filling the form linked below.
Propose CollaborationOpenFold is an open source project and anyone can help contribute directly through a pull request or issue submission.
Visit OpenFold GitHubOur goal is to develop an open ecosystem of accelerated AI for Biology tools in order to catalyze innovation, starting with state-of-the-art and permissively licensed protein structure prediction training and inference pipelines and models.
The founding members of the consortium span academia, technology corporations, startups, and pharmaceutical companies. The launching group includes the AlQuraishi lab at Columbia University, Arzeda, Cyrus Biotechnology and Outpace Bio with many more to come, so stay tuned and get in touch! The OpenFold Consortium is hosted by the Open Molecular Software Foundation.
All are welcome! Member organizations are expected to commit resources to our shared goals and mission. Many types of contributions are welcome, including but not limited to in-kind, FTE, or monetary support. If this is you and your organization, please get in touch by filling out the interest form.
The initial training and inference pipeline code is available today on our GitHub! We are currently working to optimize the codebase for large-scale training with our technology partners, which we will make publicly available as significant milestones are achieved. Over the next months, we will release the first freely licensed models for general use. OpenFold training data will be made available in the Registry of Open Data on AWS (RODA) thanks to their Open Data Sponsorship Program.
