Artem Kushner retweeted
The hallmark of scientific veracity is replication, not peer review.
H Doris. I realise astera are posting on biorxiv instead of journals but surely such a claim needs peer review. If you would like us to arrange review at @eLife we’d be happy to do it :)
Artem Kushner retweeted
Introducing Langevin Flow Maps
TLDR: Neural Network Potentials that learn their own stochastic pathwise solution for faster simulation.
- Transferable across molecular systems.
- Transition dynamics are preserved.
- No trajectory data are required.
Work together with @sgmccallum, supervised by @AlexanderTong7, and @JamesFosterBath.
Arxiv: arxiv.org/abs/2610.05998
Data: huggingface.co/datasets/nikl…
Code: github.com/sammccallum/lfm
Artem Kushner retweeted
recommended reading. liking this perspective.
smallcultfollowing.com/babys…
Artem Kushner retweeted
Introducing the embodied coding agent canvas I've been working on, and how it integrates with per-data-type GUI generation to support more transparent type-directed agent dev processes. Remote demo for ARIA SGAI
youtube.com/watch?v=U6lYtUdw…
Artem Kushner retweeted
Challenge 3 of the Anthropic × Adaptyv Protein Design Competition is open. You have to design a protein that switches on GLP1R, the receptor that semaglutide (Ozempic, Wegovy) and tirzepatide act on in type 2 diabetes and obesity.
Artem Kushner retweeted
A hard problem building on an early pioneering idea from Pilar Cossio and Gerhard Hummer
doi.org/10.1016/j.jsb.2013.1…
Artem Kushner retweeted
BREAKING: The U.S. government, Google, Meta & Zuckerberg-backed @biohub are launching a $1.8B push to build the data layer for AI biology.
The goal is to create the largest open, AI-ready biological dataset ever assembled, giving models the data needed to better predict how biology works & ultimately help treat disease.
The coalition is massive:
› Biohub committed $500M
› DOE is investing $500M+ across measurement, modeling & compute
› NIH is bringing $500M+ of federally funded datasets & repositories
› Meta, Google DeepMind & @IsomorphicLabs are jointly investing another $300M
This is effectively an infrastructure buildout for AI biology.
Instead of just building bigger models, they’re attacking one of the biggest bottlenecks in biology AI today, generating standardized, high-quality biological data at massive scale & making it openly available to researchers.
$1.8B committed to turning biology into something AI can increasingly model, predict & engineer.
Artem Kushner retweeted
Our first protein 🥩 design paper! 🤠
Check out Minkyu's tour de force on T-REX 🦖, an agentic protein design controller for de novo binder design!
TLDR we treat protein design as an online allocation problem over heterogeneous tools (Proteina-complexa, Boltzgen, Bindcraft, ✨ etc), use 🤖 agents to reason over what tools + settings to run, designed a granular classification system to manage the agents (e.g. Rescue🛟/Explore🔎/eXploit🚀), that is all orchestrated with a deterministic controller to efficiently brrr the GPUs. 💸
T-REX 🦖 is fully open source: github.com/ml-struct-bio/T-R…
Preprint: biorxiv.org/content/10.64898…
Please let us know if you have any feedback!
Artem Kushner retweeted
"No man ever runs an experiment on the same infra twice, for it's not the same infra and he's not the same man." - Heraclitus
Artem Kushner retweeted
ʙɪɴᴅᴄʀᴀꜰᴛ2 is out, and we're not waiting for the paper. The full code drops today, free for academic and industry use.
We're releasing it early so you can start designing right now, and bring its full power to the current Adaptyv competition.
github.com/PacesaLab/BindCra…
Artem Kushner retweeted
We’re partnering with @Anthropic to launch the biggest Protein Design Competition in the world, challenging people around the world to use AI to design new potential drug candidates for diseases that affect millions of lives.
The competition will feature five challenges, each focused on a specific disease or biological mechanism. Compared to previous competitions, it will be a big step-up in complexity and scale to push the boundaries of AI-driven protein design.
Together with Anthropic, we’re sponsoring over $1 million in experimental validation, making it possible to test more than 5,000 protein designs in our automated lab at no cost to participants. Anthropic is providing an additional $1 million in Claude credits.
All experimental results will be published openly on @Proteinbase, including designs that didn’t work, so anyone can access the data and build on what we learn.
The competition is open to everyone and free to enter. It will feature 3 tracks:
- Track 1 is aimed at expert protein designers, with up to 20 teams to be selected.
- Track 2 is targeting life science academics and industry researchers.
- Track 3 is open to everyone from tech enthusiasts to high-school students.
By combining Anthropic’s models with access to our automated lab, we want to make it possible for anyone with a laptop and an internet connection to join the global effort to advance human health with AI.
A big thanks to @Modal for contributing compute for protein design and to @TwistBioscience for contributing the DNA for the experimental validation!
Sign up link below -
Artem Kushner retweeted
When we design molecule we often want to target properties which are averages for the Boltzmann ensemble, rather than a single configuration or graph.
Yet, current property-guided 3D generative models condition on single conformers.
We target this with Boltzmann-Expected Design
Artem Kushner retweeted
Proteins are always moving. They wiggle. They jiggle. They interact with their surroundings. That movement drives their function.
Yet the motion stays mostly invisible. We cannot understand protein function, engineer it, or correct it until we can see the motion and predict it. prismscience.org
Artem Kushner retweeted
beautiful beautiful beautiful essay
archive.nytimes.com/opiniona…
Artem Kushner retweeted
"openai engineer explains /why/ he didn't need to understand the kernel line by line" 🙃
we're doing compilers 2.0
Artem Kushner retweeted
Watch the story of how the Model Hardware Standard began as part of our collaboration with @hhmi_science
Artem Kushner retweeted
We raised a $40M Series A to build the automated lab for agentic biology.
Last week, we shared the work we did with @Anthropic: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back.
We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating.
But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”.
That’s exactly what we’re doing at Adaptyv.
Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like @chaidiscovery and @boltz_bio to pharmas like @Roche and @novonordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.
Artem Kushner retweeted
Tackling a 60-year-old challenge in quantum chemistry: making density functional theory scale nearly linearly with system size.
This has huge implications for opening the door to realistic systems that have traditionally been too expensive to simulate.
AI has attempted to accelerate these calculations, but models generally struggle to extrapolate, particularly to systems larger than those seen during training. Unlike text or images, quantum-mechanical training data is extremely expensive to generate.
We present a single unified AI model that performs quantum-mechanical simulations of both molecules and materials in quasi-linear time.
Using a novel Fourier neural operator variant, we learn the underlying Kohn–Sham equation map to produce physics-informed, self-consistent answers.
Unlike prior AI approaches that directly predict chemical properties, our model works through intermediate steps to improve difficult predictions, resembling inference-time reasoning in large language models.
To demonstrate its scalability, we run a self-consistent calculation of a magnesium dislocation with about 80k electrons on a single GPU, which previously required about 7k GPUs.
tensorlab.cms.caltech.edu/us…
@DanishK42 @Caltech
Artem Kushner retweeted
Breakthroughs in AI for biology are largely catalyzed by the diverse datasets deposited by scientists.
To make experimental data on biomolecular dynamics from NMR as easily accessible as the PDB, @HWaymentSteele and I are releasing makeshift, installable via PyPi
Preprint: biorxiv.org/content/10.64898…
Docs: makeshift-docs.readthedocs.i…
