This post was co-written with Sachin Kadyan, a leading developer of OpenFold.
In drug discovery, understanding the 3D structure of proteins is key to assessing the ability of a drug to bind to it, directly impacting its efficacy. Predicting the 3D protein form, however, is very complex, challenging, expensive, and time consuming, and can take years when using traditional methods such as X-ray diffraction. Applying machine learning (ML) to predict these structures can significantly accelerate the time to predict protein structures—from years to hours. Several high-profile research teams have released algorithms such as AlphaFold2 (AF2), RoseTTAFold, and others. These algorithms were recognized by Science magazine as the 2021 Breakthrough of the Year.
OpenFold, developed by Columbia University, is an open-source protein structure prediction model implemented with PyTorch. OpenFold is a faithful reproduction of the AlphaFold2 protein structure prediction model, while delivering performance improvements over AlphaFold2. It contains a number of

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