Starkly Speaking tomorrow: Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction
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Hannes Stärk
Jun 16, 2025, 11:56:53 AMJun 16
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Hi together,
Tomorrow we discuss Boltz-2!
We also have a luma event in case you find that convenient: https://lu.ma/4bpuwbsr
Paper:
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction http://jeremywohlwend.com/assets/boltz2.pdf (Saro Passaro, Gabriele Corso, Jeremy Wohlwend, Mateo Reveiz, Stephan Thaler, Vignesh Ram Somnath, Noah Getz, Tally Portnoi, Julien Roy, Hannes Stark, David Kwabi-Addo, Dominique Beaini, Tommi Jaakkola, Regina Barzilay)
Accurately modeling biomolecular interactions is a central challenge in modern biology. Whilerecent advances, such as AlphaFold3 and Boltz-1, have substantially improved our ability to predict biomolecular complex structures, these models still fall short in predicting binding affinity, acritical property underlying molecular function and therapeutic efficacy. Here, we present Boltz-2,a new structural biology foundation model that exhibits strong performance for both structure andaffinity prediction. Boltz-2 introduces controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, andis, to our knowledge, the first AI model to approach the performance of free-energy perturbation(FEP) methods in estimating small molecule–protein binding affinity. Crucially, it achieves strongcorrelation with experimental readouts on many benchmarks, while being at least 1000× more computationally efficient than FEP. By coupling Boltz-2 with a generative model for small molecules,we demonstrate an effective workflow to find diverse, synthesizable, high-affinity binders, as estimated by absolute FEP simulations on the TYK2 target. To foster broad adoption and furtherinnovation at the intersection of machine learning and biology, we are releasing Boltz-2 weights,inference, and training code 1 under a permissive open license, providing a robust and extensiblefoundation for both academic and industrial research.
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction http://jeremywohlwend.com/assets/boltz2.pdf (Saro Passaro, Gabriele Corso, Jeremy Wohlwend, Mateo Reveiz, Stephan Thaler, Vignesh Ram Somnath, Noah Getz, Tally Portnoi, Julien Roy, Hannes Stark, David Kwabi-Addo, Dominique Beaini, Tommi Jaakkola, Regina Barzilay)
Accurately modeling biomolecular interactions is a central challenge in modern biology. Whilerecent advances, such as AlphaFold3 and Boltz-1, have substantially improved our ability to predict biomolecular complex structures, these models still fall short in predicting binding affinity, acritical property underlying molecular function and therapeutic efficacy. Here, we present Boltz-2,a new structural biology foundation model that exhibits strong performance for both structure andaffinity prediction. Boltz-2 introduces controllability features including experimental method conditioning, distance constraints, and multi-chain template integration for structure prediction, andis, to our knowledge, the first AI model to approach the performance of free-energy perturbation(FEP) methods in estimating small molecule–protein binding affinity. Crucially, it achieves strongcorrelation with experimental readouts on many benchmarks, while being at least 1000× more computationally efficient than FEP. By coupling Boltz-2 with a generative model for small molecules,we demonstrate an effective workflow to find diverse, synthesizable, high-affinity binders, as estimated by absolute FEP simulations on the TYK2 target. To foster broad adoption and furtherinnovation at the intersection of machine learning and biology, we are releasing Boltz-2 weights,inference, and training code 1 under a permissive open license, providing a robust and extensiblefoundation for both academic and industrial research.
Meeting Details:
Every Monday at 12:00 ET / 9:00 PT / 18:00 CE(S)T.
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
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Slack Workspace for discussion and paper voting:
https://join.slack.com/t/logag/shared_invite/zt-2zuxi7gd1-rLUgxg6gnCkhO7WlRsyElg
All information: Schedule of upcoming papers, recordings, etc.:
https://portal.valencelabs.com/starklyspeaking
Every Monday at 12:00 ET / 9:00 PT / 18:00 CE(S)T.
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Add it to your calendar:
Subscribe via Google Calendar, or subscribe via iCal.
Alternatively, add the events, or add this single event.
Slack Workspace for discussion and paper voting:
https://join.slack.com/t/logag/shared_invite/zt-2zuxi7gd1-rLUgxg6gnCkhO7WlRsyElg
All information: Schedule of upcoming papers, recordings, etc.:
https://portal.valencelabs.com/starklyspeaking
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