Rishwanth Raghu

14 posts

Rishwanth Raghu

Rishwanth Raghu

@rishwanth_raghu

CS PhD student @Princeton. Machine learning for structural biology.

Katılım Temmuz 2022
39 Takip Edilen190 Takipçiler
Rishwanth Raghu
Rishwanth Raghu@rishwanth_raghu·
We’ll be presenting CryoBoltz ❄️⚡️ at #NeurIPS this week! Check out the new experiments in our updated paper. We’re also releasing our code for the community to try out on their data 🚀 Paper: arxiv.org/abs/2506.04490 Code: github.com/ml-struct-bio/…
Rishwanth Raghu@rishwanth_raghu

Excited to present CryoBoltz ❄️⚡, a multiscale guidance approach for steering AlphaFold3/Boltz-1 to sample structures that are consistent with experimental cryo-EM density maps. 🧵1/7 arxiv.org/abs/2506.04490 Joint work with @axlevy0 @GordonWetzstein & @ZhongingAlong!

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Rishwanth Raghu retweetledi
Omenn-Darling Bioengineering Institute
Rishwanth Raghu kicks off our BioE Lunch and Learn 🍕 series for the semester! Rish is a graduate student in @ZhongingAlong’s lab and presenting “Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data”
Omenn-Darling Bioengineering Institute tweet media
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Rishwanth Raghu
Rishwanth Raghu@rishwanth_raghu·
@venzelele Thanks! We did not try other data modalities, but SAXS or any other measurement with a differentiable forward model could be used to define a new guidance target
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Rishwanth Raghu
Rishwanth Raghu@rishwanth_raghu·
For a detailed description of our approach and analysis, check out our preprint! Predicting conformational dynamics is a key challenge for structural biology. CryoBoltz is our attempt to unlock the full potential of structure prediction models using experimental data. 🚀 7/7
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Rishwanth Raghu
Rishwanth Raghu@rishwanth_raghu·
We also demonstrate our method on real cryo-EM data of P-glycoprotein. CryoBoltz models 4 conformational states of this transporter given experimental maps, whose resolutions are too low (> 4Å) for model building algorithms. 6/7
Rishwanth Raghu tweet media
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Ellen Zhong
Ellen Zhong@ZhongingAlong·
🚨New paper at #NeurIPS2024! We present Hydra, a mixture of neural fields 🐉🐉🐉 for ab initio cryo-EM reconstruction of complex mixtures. Hydra is our attempt at pushing the boundaries for even more extreme forms of heterogeneity in cryo-EM 🤯. This was joint work with an awesome team, led by @axlevy0 @rishwanth_raghu & David Shustin. Paper: hydra.cs.princeton.edu Poster session happening now! Come by 👋
Ellen Zhong tweet media
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