An open-source implementation of AlphaFold3
Hi HN - we’re the founders of Ligo Biosciences and are excited to share an open-source implementation of AlphaFold3, the frontier model for protein structure prediction. Google DeepMind and their new startup Isomorphic Labs, are expanding into drug discovery. They developed AlphaFold3 as their model to accelerate drug discovery and create demand from big pharma. They already signed Novartis and Eli Lilly for $3 billion - Google’s becoming a pharma company! (https://www.isomorphiclabs.com/articles/isomorphic-labs-kick...) AlphaFold3 is a biomolecular structure prediction…
In plain words
Ligo Biosciences has released an open-source implementation of AlphaFold3, a protein structure prediction model. The software predicts protein structures, drug-protein interactions, and nucleic acid-protein complex structures. It is designed for researchers and scientists working in drug discovery and structural biology who need to accelerate the mapping of biomolecular structures. The open-source approach makes the frontier model accessible beyond commercial licensing arrangements.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN - we’re the founders of Ligo Biosciences and are excited to share an open-source implementation of AlphaFold3, the frontier model for protein structure prediction. Google DeepMind and their new startup Isomorphic Labs, are expanding into drug discovery. They developed AlphaFold3 as their model to accelerate drug discovery and create demand from big pharma. They already signed Novartis and Eli Lilly for $3 billion - Google’s becoming a pharma company! (https://www.isomorphiclabs.com/articles/isomorphic-labs-kick...) AlphaFold3 is a biomolecular structure prediction model that can do three main things: (1) Predict the structure of proteins; (2) Predict the structure of drug-protein interactions; (3) Predict nucleic acid - protein complex structure. AlphaFold3 is incredibly important for science because it vastly accelerates the mapping of protein structures. It takes one PhD student their entire PhD to do one structure. With AlphaFold3, you get a prediction in minutes on par with experimental accuracy. There’s just one problem: when DeepMind published AlphaFold3 in May (https://www.nature.com/articles/s41586-024-07487-w), there was no code. This brought up questions about reproducibility (https://www.nature.com/articles/d41586-024-01463-0) as well as complaints from the scientific community (https://undark.org/2024/06/06/opinion-alphafold-3-open-sourc...). AlphaFold3 is a fundamental advance in structure modeling technology that the entire biotech industry deserves to be able to reap the benefits from. Its applications are vast, including: - CRISPR gene editing technologies, where scientists can see exactly how the DNA interacts with the scissor Cas protein; - Cancer research - predicting how a potential drug binds to the cancer target. One of the highlights in DeepMind’s paper is the prediction of a clinical KRAS inhibitor in complex with its target. - Antibody / nanobody to target predictions. AlphaFold3 improves accuracy on this class of molecules 2 fold compared to the next best tool. Unfortunately, no companies can use it since it is under a non-commercial license! Today we are releasing the full model trained on single chain proteins (capability 1 above), with the other two capabilities to be trained and released soon. We also include the training code. Weights will be released once training and benchmarking is complete. We wanted this to be truly open source so we used the Apache 2.0 license. Deepmind published the full structure of the model, along with each components’ pseudocode in their paper. We translated this fully into PyTorch, which required more reverse engineering than we thought! When building the initial version, we discovered multiple issues in DeepMind’s paper that would interfere with the training - we think the deep learning community might find these especially interesting. (Diffusion folks, we would love feedback on this!) These include: - MSE loss scaling differs from Karras et al. (2022). The weighting provided in the paper does not downweigh the loss at high noise levels. - Omission of residual layers in the paper - we add these back and see benefits in gradient flow and convergence. Anyone have any idea why Deepmind may have omitted the residual connections in the DiT blocks? - The MSA module, in its current form, has dead layers. The last pair weighted averaging and transition layers cannot contribute to the pair representation, hence no grads. We swap the order to the one in the ExtraMsaStack in AlphaFold2. An alternative solution would be to use weight sharing, but whether this is done is ambiguous in the paper. More about those issues here: https://github.com/Ligo-Biosciences/AlphaFold3 How this came about: we are building Ligo (YC S24), where we are using ideas from AlphaFold3 for enzyme design. We thought open sourcing it was a nice side quest to benefit the community. For…
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