MIT Scientists Develop Open-Source AI Model for Accurate Protein Structure Prediction
MIT scientists have released a powerful, open-source AI model, called Boltz-1, that could significantly accelerate biomedical research and drug development.
Achieving State-of-the-Art Performance
Boltz-1 is the first fully open-source model that achieves state-of-the-art performance at the level of AlphaFold3, the model from Google DeepMind that predicts the 3D structures of proteins and other biological molecules.
The Goal: Democratizing Science
MIT graduate students Jeremy Wohlwend and Gabriele Corso were the lead developers of Boltz-1, along with MIT Jameel Clinic Research Affiliate Saro Passaro and MIT professors of electrical engineering and computer science Regina Barzilay and Tommi Jaakkola.
“We hope for this to be a starting point for the community,” Corso said. “There is a reason we call it Boltz-1 and not Boltz. This is not the end of the line. We want as much contribution from the community as we can get.”
Understanding Protein Structure
Proteins play an essential role in nearly all biological processes. A protein’s shape is closely connected with its function, so understanding a protein’s structure is critical for designing new drugs or engineering new proteins with specific functionalities.
However, accurately predicting a protein’s 3D structure has been a major challenge for decades. DeepMind’s AlphaFold2 uses machine learning to rapidly predict 3D protein structures that are so accurate they are indistinguishable from those experimentally derived by scientists. This open-source model has been used by academic and commercial research teams around the world, spurring many advancements in drug development.
Improving upon AlphaFold3
AlphaFold3 improves upon its predecessors by incorporating a generative AI model, known as a diffusion model, which can better handle the amount of uncertainty involved in predicting extremely complex protein structures. Unlike AlphaFold2, AlphaFold3 is not fully open-source, nor is it available for commercial use, which prompted criticism from the scientific community and kicked off a global race to build a commercially available version of the model.
The Boltz-1 Model
The MIT team followed the same initial approach as AlphaFold3, but after studying the underlying diffusion model, they explored potential improvements. They incorporated those that boosted the model’s accuracy the most, such as new algorithms that improve prediction efficiency.
Along with the model itself, they open-sourced their entire pipeline for training and fine-tuning so other scientists can build upon Boltz-1.
A Global Collaboration
The researchers plan to continue improving the performance of Boltz-1 and reduce the amount of time it takes to make predictions. They also invite researchers to try Boltz-1 on their GitHub repository and connect with fellow users of Boltz-1 on their Slack channel.
“We think there is still many, many years of work to improve these models. We are very eager to collaborate with others and see what the community does with this tool,” Wohlwend adds.
Conclusion
Boltz-1 is a significant achievement in the field of biomolecular structure prediction, and its open-source nature has the potential to democratize access to cutting-edge structural biology tools. The MIT team’s goal is to foster global collaboration, accelerate discoveries, and provide a robust platform for advancing biomolecular modeling.
FAQs
Q: What is Boltz-1? A: Boltz-1 is an open-source AI model for predicting the 3D structures of proteins and other biological molecules.
Q: What is the goal of Boltz-1? A: The goal of Boltz-1 is to democratize access to cutting-edge structural biology tools and accelerate discoveries in the field of biomolecular structure prediction.
Q: How does Boltz-1 compare to AlphaFold3? A: Boltz-1 achieves state-of-the-art performance at the level of AlphaFold3, a model from Google DeepMind that predicts 3D protein structures.
Q: Is Boltz-1 open-source? A: Yes, Boltz-1 is fully open-source, and its entire pipeline for training and fine-tuning is available on GitHub.
