Jeremy Wohlwend
@jeremyWohlwend
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PhD Student @ MIT CSAIL, former Research Engineer at ASAPP Inc.
Joined June 2014
RT @GabriCorso: 📢 Call for proposals: Boltz small-molecule design collaboration! 🧬.Can we help design your ideal molecule? Can you help us….
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RT @felix_faltings: Excited to share our new ProxelGen paper!. Completely different from RFDiffusion etc., we generate proteins as densitie….
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Also cool to see Boltz-2 highlighted on stage today by Rory Kelleher’s keynote at #GTC2025. Read more about NVIDIA’s work from their Blog Post:
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All of this translates in end to end speedups that range from 1.5 to over 2x, and even higher for long sequences, with no loss in accuracy!.You can try it out directly on our repo, updating to version 2.1.1.
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Some speed and memory measurements I ran on the Pairformer layer. Ran with no auto-tuning on an A6000 card.
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Two new kernels: triangular attention and triangular multiplicative layer. While there have been efforts to build kernels for the triangular attention, there hasn’t been as much emphasis on accelerating the triangular multiplicative layer. Both support training and inference!.
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The team at @NVIDIA has done such amazing work accelerating Boltz-2 through novel CUDA kernels and deploying Boltz-2 as NVIDIA’s NIM! The kernels are live on the Boltz repo, and you can run the model with 2x training & inference speedup and large memory savings!🧵#cuEquivariance
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RT @NVIDIAHealth: Did you know the NVIDIA #cuEquivariance library can now accelerate Triangle Attention and Triangle Multiplication operati….
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RT @AIHealthMIT: A remarkable turnout! Thank you to the 400+ attendees who came to listen to the first-ever Boltz-2 presentation in-person….
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Boltz-1 was always a stepping stone. It’s great to finally share what we’ve been up to since. Boltz-2 puts binding affinity prediction to work—so we can run virtual screens without breaking a sweat. A few other goodies too, go check it out!.
Excited to unveil Boltz-2, our new model capable not only of predicting structures but also binding affinities! Boltz-2 is the first AI model to approach the performance of FEP simulations while being more than 1000x faster! All open-sourced under MIT license! A thread… 🤗🚀
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RT @GabriCorso: Excited to unveil Boltz-2, our new model capable not only of predicting structures but also binding affinities! Boltz-2 is….
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Thrilled to release Boltz-1x! The physical quality of poses was a recurring piece of feedback we received. With Boltz-1x, we use inference-time steering to remove clashes, respect chirality and other physical properties. A few other goodies too. Work led by the amazing @NoahBGetz.
🚀 Excited to release a major update to the Boltz-1 model: Boltz-1x!. Boltz-1x introduces inference-time steering for much higher physical quality, CUDA kernels for faster, more memory-efficient inference and training, and more! 🔥🧵
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RT @TheMarkFdn: 🤯Mind-blowing keynote by @BarzilayRegina on the application of AI in cancer research! 🤖The future is here. @MIT @AIHealthMI….
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RT @peholderrieth: New paper out!. We introduce “LEAPS”, a neural sampling algorithm for discrete distributions via continuous-time Markov….
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RT @Itamarchinn: Excited to see our work published in @ScienceMagazine on generating novel disordered proteins with PLMs that selectively l….
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RT @AIHealthMIT: As lung cancer rates rise in nonsmokers, a troubling trend for the #1 deadliest cancer, we hope more hospitals will adopt….
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RT @GabriCorso: The recording of our seminar on Boltz-1 with @mmbronstein and @vant_ai is now public on YouTube!
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Check out our repo for all code, data, and instructions: our updated manuscript: and join the Boltz Slack community: Merry Christmas from the Boltz team with @GabriCorso and @pas_saro!.
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Finally, to encourage transparent benchmarking in the community, we released a full set of evaluations on PDB test set and CASP15 for Boltz-1, Chai-1 and AlphaFold3! All the instructions at
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