We trained a bidirectional masked generative model on protein sequence, structure and function. ESM3 work over tokenized representations of multiple modalities and can generate proteins with high fidelity and controllability. ESM3 further improves with feedback using alignment methods similar to Reinforcement Learning from Human Feedback (RLHF) applied in LLMs.
We have prompted ESM3 to generate fluorescent proteins with a chain of thought. Among the generations that we synthe- sized, we found a bright fluorescent protein at far distance (58% identity) from known fluorescent proteins. Similarly distant natural fluorescent proteins are separated by over five hundred million years of evolution.
We trained a bidirectional masked generative model on protein sequence, structure and function. ESM3 work over tokenized representations of multiple modalities and can generate proteins with high fidelity and controllability. ESM3 further improves with feedback using alignment methods similar to Reinforcement Learning from Human Feedback (RLHF) applied in LLMs.
We have prompted ESM3 to generate fluorescent proteins with a chain of thought. Among the generations that we synthe- sized, we found a bright fluorescent protein at far distance (58% identity) from known fluorescent proteins. Similarly distant natural fluorescent proteins are separated by over five hundred million years of evolution.
- Check out our github repository: https://github.com/evolutionaryscale/esm
- Read the paper: https://www.evolutionaryscale.ai/papers/esm3-simulating-500-...
- And make your own proteins on Colab: https://colab.research.google.com/github/evolutionaryscale/e...