Cambridge (USA)
Joined March 2012
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Picornaviruses infect 10’s of millions of children every year. We know the sequences but not the structures. Mapping them is key in understanding how they function.
So, we mapped all the sequences of Picornaviruses we could find. ~75’000 Structures.
Enjoy, it's all Open Source
Pierre Bongrand retweeted
Tatta Bio is hiring a biochemist to help build our AI-guided enzyme discovery platform! Please share with anyone in your network who might be interested.
We're hiring a Lead Scientist, focused on biocatalysis, at Tatta Bio in Cambridge, MA!
We're looking for a hands-on scientist to lead the development of our high-throughput enzyme characterization platform, working closely with our ML team. If you have deep expertise in biochemistry and analytical chemistry, enjoy building experimental workflows, and want to help shape a growing research program, we'd love to hear from you.
Check out the job description below to learn more and apply!
tatta-bio.notion.site/Lead-S…
Pierre Bongrand retweeted
Language models can learn new tasks from just a few examples in their prompt, with no retraining. This is called in-context learning (ICL). Now genome language models can do it too.
With a handful of examples, Evo 2 ICL generates proteins, de novo antibodies, CRISPR-Cas9 loci, mitochondrial genomes and entire bacteriophages.
We can even use the synthetic sequences iteratively to construct a chain-of-thought (CoT) and achieve harder design goals.
New work with Yukun A. Hao, @BrianHie and team 🧵
Pierre Bongrand retweeted
I am often being asked “Should we slow down AI development in biology? “ or “What is your take on biology being solved by AI?”
My answer has always been: we need to move faster. Patients are waiting, and many are running out of time.
Consider the starting point:
• About 95% of rare diseases have no approved treatment.
• Developing a new treatment typically takes 10–15 years.
• Cancer and Cardiovascular diseases remain top killers globally .
Every year we can remove from drug discovery matters to someone who may not have another year.
This is why I work on AI for biology. Virtual cells, AI-designed molecules and agents that help scientists plan experiments offer a chance to test more ideas, identify failures earlier and learn faster.
We still have to prove that these tools produce better medicines. Accelerating discovery must go hand in hand with rigorous clinical evidence and safeguards against misuse.
But the cost of moving too slowly belongs in this conversation too.
For a patient who has exhausted every available treatment, the status quo is already dangerous.
Let’s build with the urgency that deserves.
Pierre Bongrand retweeted
We’re releasing a broad range of new mathematical results produced by an internal frontier model.
We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.
github.com/openai/math
Sure, Mistral isn't pushing the frontier on dollar per intelligence. They have fewer resources and can't subsidize compute like other labs
This isn't a B2C product. It's a sovereign AI solution
Europe needs an alternative to the US & China that's not too far from the frontier
Mistral is back:
-pushing the frontier on dollar per intelligence (1.13$ per task for a 38. mimo v2.6 pro gets 46 for 0.13$)
-best open model out of us or europe (competition: nemotron 3 ultra 23, gemma 4 17, gpt-oss 12)
-same score as gpt 6 luna. luna costs 0.07$ per task. 16x
-#64 of 225 on artificial analysis. frontier
-1 trillion parameters to land 3 points under GLM 5.3 flash (320B, 0.25$)
-open weights (AA lists it as proprietary. weights oct 27)
-most open license yet (large 3 apache 2.0, medium 3.5 modified mit, this one "custom mistral license")
-only 8 points behind the best chinese open model and 20 behind opus 5.5
-beat glm 5.3 on deepswe, 62 to 61 (on mistrals chart. the live leaderboard has glm 5.3 at ~69)
-state of the art in legal (15%)
-highest cyber score of any model, 82% (opus 5.5 and astra refuse the task and score near zero)
-same blog post brags it refuses more cyber prompts than any other open model
-200M output tokens to run the index, median is 81M. le chonk talks
-+29 points over large 3 (large 3 had 9)
-sovereign ai (they resell glm 5.3 since august. until today the smartest model on mistrals own platform was chinese)
-3 billion euro raised, 21 billion valuation, first model out of it: 38
-"no signs of saturation" (the rl run isnt finished, they shipped anyway)
-the meme promised 30 trillion parameters. we got 1 (49B active)
-europe can compete, said mensch. in abu dhabi
-2nd of 5 in their own blind eval. behind opus 5. not 5.5
New Sovereign Model! Mistral reports it to be the new frontier on Legal, Finance, and equal in Cyber and code.
Meet Mistral Large 4, aka Le Chonk.
• 1T parameters, natively multimodal. 49B active.
It is the best open weights model from US or Europe on aggregated benchmarks.
• State-of-the-art on critical workloads, including cyber defense, manufacturing and finance and it surpasses closed frontier models on visual grounding.
• Forged in Europe end-to-end and is deployable from Europe via our own Mistral Cloud infrastructure.
• Available to all via API today. Working with cybersecurity partners privately.
Open weights release end of October.
All results seem to come from reasonable benchmarks. To the exception of DeepSWE v1.1 that we know is flawed, thanks to @EpochAIResearch. So would need more benchmarking. But very very exciting! Thank you @MistralAI
These early results are reported to be from Mistral, and are in this article: thenextweb.com/news/mistral-…
everyone is talking about sovereign LLMs, is @MistralAI about to release theirs?
For the non-French, the video is about Marie-José Pérec (a French runner), who comes from behind to catch everyone on the track and wins the European Championships 4×400m relay 👀
NOTE: THIS IS NOT MARIE-JOSÉ PÉREC.
THIS IS THE INCREDIBLE FLORIA GUEÏ @FloriaGuei
(my apologies..) and thanks to my fact checkers!
Please find the v2 of the paper:
biorxiv.org/content/10.64898…
The code:
github.com/rouskinlab/Albatr…
The 75'000 structures:
albatrossrna.org/
Thank you to @silvirouskin, Adam Sychla and all our co-authors for this awesome feat.
Thanks to @ChrisHayduk for Rosalind workbench btw, pretty awesome tool. Video thanks to Codex too :))
Pierre Bongrand retweeted
Re today's incident, maybe not a bad idea to check your settings (Settings → Data Controls)
Pierre Bongrand retweeted
Today is the first time our Intelligence Frontier chart has moved backward.
New paper is out.
You can learn RNA Structure from sequences alone.
TL;DR Sequence is all you need.
After fine-tuning RiNALMo on 50k IRES sequences, our model outperforms covariance, thermodynamic, and deep learning baselines on secondary structure prediction. 🧵
New preprint from the lab ! In short - nobody told the model what a stem-loop was, we gave it 50,000 IRES sequences and one job: predict masked nucleotides. That’s it! Albatross is an RNA language model that taught itself the structural logic of viral RNA the same way LLMs learn grammar- from sequence alone. No base pairing rules, no thermodynamics, no structure labels. Check it out-biorxiv.org/content/10.64898… and albatrossrna.org
Did the LLM only learn co-evolutionary signal?
-> NO.
We observed this with Gallivirus. There is no co-evolutionary signal (1 nt) but we have this beautiful structure.
You can read more about the paper here: biorxiv.org/content/10.64898…
& you can observe 75 000 dependency maps here:
albatrossrna.org
