We’re excited to introduce @ChaiDiscovery and release Chai-1, a foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of drug discovery tasks

We're releasing inference code, weights & a web interface: chaidiscovery.com/blog/introduct…
We tested Chai-1 across a number of benchmarks, and found that the model achieves a 77% success rate on the PoseBusters benchmark (vs. 76% by AlphaFold3).

📢 Our new #manuscript shows how zero-shot Generative AI can create de novo antibodies from scratch.

🏅Hundreds of antibodies are created zero-shot and validated in the wet-lab for the first time ever.

Paper: biorxiv.org/content/10.110…
Data: github.com/AbSciBio/unloc… Our zero-shot generative AI framework. The model is programmed with a target antigen structure and a chosen antibody scaffold sequence.

Antibodies are then generated de novo.

Note: all proteins binding the target (or its homologs) were removed from the training set.

Excited to share an update to our work on evolutionary-scale modeling (ESM)! Over the past year, we rewrote our paper with better pretraining and downstream models, leading to state-of-the-art results across multiple benchmarks. (1/8)
biorxiv.org/content/10.110… Last year, we showed that Transformer language models learn intrinsic properties of proteins from sequences. But on quantitative benchmarks, these models did not improve over alignment-based methods, as shown by @roshan_m_rao, et al in TAPE.😵(2/8)