🚀 Excited to release BoltzDesign1!

✨ Now with LogMD-based trajectory visualization.
🔗 Demo: rcsb.ai/ff9c2b1ee8
Feedback & collabs welcome! 🙌

🔗: GitHub: github.com/yehlincho/Bo...
🔗: Colab: colab.research.google.com/github/yehli...
@sokrypton.org @martinpacesa.bsky.social

5. BoltzDesign1 can be used to design sequences and structures that AlphaFold3 predicts to bind to metal ions, nucleic acids, and other biomolecules

4. We achieved the best results by setting the Pairformer recycling step to 0 and fixing the initial BoltzDesign1 sequence at the interface while redesigning the remaining surface regions using LigandMPNN.

3. By utilizing only the Pairformer and Confidence module, our method generates highly diverse binders, with high AlphaFold3 success rates, strong cross-model and self-consistency, as demonstrated by benchmarks on four small-molecule targets from the RFDiffusionAA benchmark set.

2. Instead of optimizing single structures, we optimize directly on the distogram, shaping the probability distributions of atomic distances. We show that the distogram effectively captures interactions between proteins and their targets, serving as a proxy for confidence scores

1. We introduce BoltzDesign1, which inverts the Boltz-1 model—an open-source reproduction of AlphaFold3—to enable the design of protein binders for diverse molecular targets without requiring model fine-tuning.

Excited to share our preprint “BoltzDesign1: Inverting All-Atom Structure Prediction Model for Generalized Biomolecular Binder Design” — a collaboration with
@martinpacesa.bsky.social, @Zhidian Zhang, @Bruno E. Correia, and @sokrypton.org

🧬 Code will be released in a couple weeks