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Grant Rotskoff

@grant.rotskoff.cc

980 followers 110 following 19 posts

Statistical mechanic working on generative models for biophysics and beyond. Assistant professor at Stanford. https://statmech.stanford.edu

statmech.stanford.edu

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Reposted by Grant Rotskoff

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Jun 19

Small angel X-ray scattering

Reposted by Grant Rotskoff

Sam Power @spmontecarlo.bsky.social · May 7

Big fan of this perspective:

Reposted by Grant Rotskoff

Andrew White 🐦‍⬛ @andrew.diffuse.one · May 1

The plan at FutureHouse has been to build scientific agents for discoveries. We’ve spent the last year researching the best way to make agents. We’ve made a ton of progress and now we’ve engineered them to be used at scale, by anyone. Free and on API.

Grant Rotskoff @grant.rotskoff.cc · May 1

What an incredibly cool paper! While knot theory strictly applies to closed curves, Tommy, @smnlssn.bsky.social , and @paulrobustelli.bsky.social show that writhe, a knot "non-invariant" that changes with smooth deformations, provides a meaningful descriptor for flexible conformations.

Paul Robustelli @paulrobustelli.bsky.social · Apr 30

Presenting one of my favorite manuscripts I've ever worked on:

"Characterizing structural and kinetic ensembles of intrinsically disordered proteins using writhe"

www.biorxiv.org/content/10.1...

by Tommy Sisk, with a generative modeling component done in collaboration with @smnlssn.bsky.social

Reposted by Grant Rotskoff

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Mar 12

Our review on machine learning methods to study sequence–ensemble–function relationships in disordered proteins is now out in COSB

authors.elsevier.com/sd/article/S...
Led by @sobuelow.bsky.social and Giulio Tesei

Figure from the paper illustrating sequence–ensemble–function relationships for disordered proteins. ML prediction (black) and design (orange) approaches are highlighted on the connecting arrows. Prediction of properties/functions from sequence (or vice versa, design) can include biophysics approaches via structural ensembles, or bioinformatics approaches via other hetero- geneous sources. The lower panels show examples of properties and functions of IDRs for predictions or design targets. ML, machine learning; IDRs, intrinsically disordered proteins and regions.

Grant Rotskoff @grant.rotskoff.cc · Mar 4

Amazingly, this trick works. Due to improved algorithms for learning the score coming from the generative modeling, the applicability of this approach is very broad. I had spent several years making false starts on implementing the Malliavin calculus, but in the end, we found a route around it ;)

Grant Rotskoff @grant.rotskoff.cc · Mar 4

Jérémie Klinger found a simple trick back to Girsanov: you take the perturbation in the diffusion at the level of the Fokker-Planck equation and rewrite it to be included in the drift. The resulting drift then has a term proportional to \nabla \log \rho(x,t), what machine learners call the score!

Grant Rotskoff @grant.rotskoff.cc · Mar 4

The “classical” strategy for diffusion sensitivities comes from financial mathematics and is called the Malliavin calculus, it’s very explicit for simple models like Black Scholes but for a general Langevin equation, it is no easy feat to compute the sensitivity.

Grant Rotskoff @grant.rotskoff.cc · Mar 4

In many biological and active systems, diffusivity is highly spatially dependent, and the theory for perturbations in such cases is rather limited, largely based on beautiful work by Leticia Cugliandolo and work by Falasco and Baeisi, among many others.

Grant Rotskoff @grant.rotskoff.cc · Mar 4

Far from equilibrium, it is not so easy: one needs to understand the dynamics, and this requires working with dynamical trajectories and their associated path measures. Classically, we do this using the Girsanov theorem, which constructs a “relative path measure” as we perturb the drift term.

Grant Rotskoff @grant.rotskoff.cc · Mar 4

Computing response functions or “sensitives” requires understanding how an external perturbation drives the change in some observable. For equilibrium systems, Onsager taught us that this can be understood with correlation functions.

Grant Rotskoff @grant.rotskoff.cc · Mar 4

Excited to see our paper “Computing Nonequilibrium Responses with Score-Shifted Stochastic Differential Equations” in Physical Review Letters this morning as an Editor’s Suggestion! We uses ideas from generative modeling to unravel a rather technical problem. 🧵 journals.aps.org/prl/abstract...

Reposted by Grant Rotskoff

Sam Rodriques @sgrodriques.bsky.social · Jan 31

Applications for the FutureHouse Independent Postdoctoral Fellowship are due in two weeks! $125k annual stipend, full access to our resources, be coadvised by world class professors and apply our AI science agents to make new discoveries. Apply!

Details here: www.futurehouse.org/fellowship

Reposted by Grant Rotskoff

Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · Jan 26

Ten simple rules for developing good reading habits during graduate school and beyond

To me, the most important are:
Read often, read broadly (incl. older papers and outside your field), and learn to read some papers in detail and others more superficially (and quickly)

Ten simple rules for developing good reading habits during graduate school and beyond by Marcos Méndez
1: Develop the habit of reading on a daily basis
2: Read thoroughly to build a sound background understanding of your topic
3: Do not ignore the pillars of your discipline; read the classics
4: If you have to get familiar with a new topic, consider reading in chronological order
5: Avoid narrow-mindedness by reading beyond your discipline
6: Create a list of relevant journals
7: Not all interesting stuff will appear in articles; read books
8: Use a reference manager to keep track of your literature
9: Keep a long-term review for your own use as a way to remember what you read
10: Build your own library to make yourself independent and inspire others

Reposted by Grant Rotskoff

Laura M. K. Dassama @lmkdassama.bsky.social · Jan 18

Excited to share this beast of a review on the potential of protein-based degraders from trainees who are all not on the Sky! Herein we cover choice of binders, selection strategies, E3 ligases, and DELIVERY! pubs.acs.org/doi/10.1021/...

Protein-Based Degraders: From Chemical Biology Tools to Neo-Therapeutics

The nascent field of targeted protein degradation (TPD) could revolutionize biomedicine due to the ability of degrader molecules to selectively modulate disease-relevant proteins. A key limitation to ...

Grant Rotskoff @grant.rotskoff.cc · Dec 23

I am hiring a postdoctoral scholar with a start date summer or fall 2025. Projects will be focused on thermodynamically consistent generative models, broadly defined. If you’re interested, please send a CV and one paragraph about why you think you’d be a good fit to [email protected]

Grant Rotskoff @grant.rotskoff.cc · Dec 22

Lot of cool stuff in here. Consistent with my working hypothesis that the main scientific utility of LLMs at the moment is plain old NLP

Kevin Jablonka @handle.invalid · Dec 21

🎅🏼 A small early Christmas present from our team.

To celebrate the publication of our data extraction tutorial in Chem Soc Rev, we made it easy to run it — without any installation — on a JupyterHub of the Base4NFDI.

🎥 Video intro to the JupyterHub deployment: youtu.be/l-5QNUo1fcU

From text to insight: large language models for chemical data extraction

The vast majority of chemical knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on m...

Grant Rotskoff @grant.rotskoff.cc · Dec 19

Really cool opportunity via futurehouse. Come work with them and collaborate with us at Stanford!

Sam Rodriques @sgrodriques.bsky.social · Dec 19

FutureHouse is launching an independent postdoctoral fellowship program for exceptional researchers who want to apply our automated science tools to specific problems in biology and biochemistry, in collaboration with world-leading academic labs. 1/

Grant Rotskoff @grant.rotskoff.cc · Dec 13

If you didn't see our poster at NeurIPS on how to make diffusion model inference fast, you can always read the paper here: arxiv.org/abs/2405.15986

Reposted by Grant Rotskoff

Rommie Amaro @rommieamaro.bsky.social · Dec 9

also I must say often when I read new methods being pre-printed, while I appreciate the eagerness to make a splash, many folks seem unaware of the long history of this field & its assessments - to their detriment

If in CADD, pls read through D3R's last paper
pubmed.ncbi.nlm.nih.gov/31974851/

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies - PubMed

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the resul...

pubmed.ncbi.nlm.nih.gov

Grant Rotskoff @grant.rotskoff.cc · Dec 7

Can verify that the code works, too :)

Grant Rotskoff @grant.rotskoff.cc · Dec 6

@franknoe.bsky.social presented this very impressive work at a fantastic @cecamevents.bsky.social workshop this week. I’m very excited to take a deep dive into the details this weekend!

Frank Noe @franknoe.bsky.social · Dec 6

Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @msftresearch.bsky.social ch AI for Science.

www.biorxiv.org/content/10.1...

Grant Rotskoff @grant.rotskoff.cc · Dec 3

If you're at NeurIPS next week come see our spotlight poster led by Yinuo Ren and Haoxuan Chen! We use the parallel sampling technique to rigorously establish a big acceleration for diffusion model inference! neurips.cc/virtual/2024...

NeurIPS Poster Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time ComplexityNeurIPS 2024

Grant Rotskoff @grant.rotskoff.cc · Dec 2

If you're talking about order in the dynamics itself, then dynamical large deviation theory is very relevant and the canonical review is by Hugo Touchette arxiv.org/abs/0804.0327

The large deviation approach to statistical mechanics

The theory of large deviations is concerned with the exponential decay of probabilities of large fluctuations in random systems. These probabilities are important in many fields of study, including st...

Grant Rotskoff @grant.rotskoff.cc · Dec 2

There's no easy way to do this in general, but computing the stationary distribution for a nonequilibrium dynamics might be a possible in some low dimensional system or systems with special structure ( ones where you can represent the distribution with tensor networks). Simulations otherwise...