Hannes Stark
hannes-stark.bsky.social
Hannes Stark
@hannes-stark.bsky.social
MIT PhD Student - ML for biomolecules - https://hannes-stark.com/
the "the first evidence " is incorrect. We removed it from the paper.
October 27, 2025 at 12:55 AM
David M. Sabatini, William F. DeGrado, Jeremy Wohlwend, Gabriele Corso, Regina Barzilay, Tommi Jaakkola
October 26, 2025 at 10:40 PM
Arash Vahdat, Shamayeeta Ray, Jonathan T. Goldstein, Andrew Savinov, Jacob A. Hambalek, Anshika Gupta, Diego A. Taquiri-Diaz, Yaotian Zhang, A. Katherine Hatstat, Angelika Arada, Nam Hyeong Kim, Ethel Tackie-Yarboi, Dylan Boselli, Lee Schnaider, Chang C. Liu, Gene-Wei Li, Denes Hnisz,
October 26, 2025 at 10:40 PM
Huge thanks to @aihealthmit.bsky.social
This is an awesome team: Felix Faltings, MinGyu Choi, Yuxin Xie, Eunsu Hur, Timothy O’Donnell, Anton Bushuiev, Talip Uçar, Saro Passaro, Weian Mao, Mateo Reveiz, Roman Bushuiev, Tomáš Pluskal, Josef Sivic, Karsten Kreis,
October 26, 2025 at 10:40 PM
And join us for live presentations, demos, and discussions:
MIT (Cambridge) – Thursday, October 30th luma.com/7474iho2
London – Thursday, November 6th luma.com/l2zgvfwt
BoltzGen MIT Presentation · Luma
Join us in room 32-123 as we will share details of our new model BoltzGen and discuss the future of biomolecular design 🧬 After the presentation and Q&A,…
luma.com
October 26, 2025 at 10:40 PM
🚀 Model & code: github.com/HannesStark/...
🤗 Join our fast-growing Slack community: boltz.bio/join-slack
🧠 Blog post: boltz.bio/boltzgen
📄 Full manuscript: hannes-stark.com/assets/boltz...
GitHub - HannesStark/boltzgen
Contribute to HannesStark/boltzgen development by creating an account on GitHub.
github.com
October 26, 2025 at 10:40 PM
Everything is integrated in one easy to use end-to-end pipeline! Just type up your design specification and try it out!
October 26, 2025 at 10:40 PM
This results in a design specification language for various constraints – including covalent bonds, structure groups, binding sites, secondary structures and design masks – that steer the diffusion process towards specific design objectives during inference.
October 26, 2025 at 10:40 PM
With this we train a model with the standard AF3 / Boltz-2 scalable architecture that has proven state-of-the-art for folding. Injecting conditioning inputs allows us to control the designed binder in various ways
October 26, 2025 at 10:40 PM
Due to a purely geometry-based encoding of designed residues this unified boils down to supervising with the same diffusion loss for structure prediction and design.
October 26, 2025 at 10:40 PM
BoltzGen’s success stems from its unification of design and structure prediction. A purely geometry-based representation of designed residues enables scalable training on both tasks. As a result, unlike any previous design model, BoltzGen matches the performance of SOTA folding
October 26, 2025 at 10:40 PM
We have more campaigns where the validation goes beyond binding: we test 6 BoltzGen designs against each of 3 diversely structured peptides. We obtain nM binders against two and uM against the third. For every target, at least on design neutralizes its antimicrobial activity.
October 26, 2025 at 10:40 PM
For peptides, we e.g., succeed for designing linear and disulfide-bonded peptides against crucial metabolic pathway targets and disordered proteins. When testing 5 designs, we have one success, posing the first evidence of de-novo peptides binding disordered proteins.
October 26, 2025 at 10:40 PM
We go after targets that require generalization. E.g. we tested 15 nanobodies against each of 9 targets selected for their dissimilarity to any protein with an existing bound structure. For 6 of 9 targets we obtain nM binders. The same 67% success rate holds for miniproteins 🤗
October 26, 2025 at 10:40 PM