🧬MTS @ Latent Labs 💡GenAI for Biology & Protein Design 🎓PhD in ML @ University of Cambridge ⚙️ Ex-RS Intern @ Apple MLR & Microsoft Research
London, England
Joined October 2020
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Super proud of the team's achievement 🚀
Shipping our second model Latent-X2 that allows users to design antibody binders on our platform. Check the below thread for more details 👇
Jonathan Crabbé retweeted
Today we're launching Latent-Y: the world's first autonomous agent for drug design, lab-validated end to end.
Give it a research goal. Latent-Y reasons, designs, iterates, and delivers lab-ready antibodies, autonomously or collaboratively, with the biological reasoning of a PhD protein design expert.
Technical report: tinyurl.com/latent-y-techrep…
Blog post: latentlabs.com/latent-y
Apply for access: platform.latentlabs.com
Jonathan Crabbé retweeted
Introducing Latent-X2 — AI-generated antibodies with drug-like developability and low immunogenicity in human panels, zero-shot.
Technical report: tinyurl.com/latent-X2-report
Blog: latentlabs.com/latent-x2
Apply for access: [email protected]
Jonathan Crabbé retweeted
5 months from Latent-X1 to Latent-X2. AI-generated antibodies with drug-like developability and low immunogenicity in human panels — zero-shot.
Available now for selected partners.
Jonathan Crabbé retweeted
Latent-X has been available for a little over two weeks, and we couldn't be more excited about the science that is already being done with it on our platform.
Our CEO @saakohl joined @Bloomberg News Daybreak Europe this morning to talk about why we're building it, and the impact it is already having.
Watch the video here: youtube.com/watch?v=PW6NQ1X2…
Jonathan Crabbé retweeted
Showcasing Latent-X's breakthrough macrocycle designs.
Our AI achieved an unprecedented 91-100% hit rate with single-digit micromolar binding affinities—functional binders on every single target tested.
Explore our binders without logging in: platform.latentlabs.com
Jonathan Crabbé retweeted
The first 3 days have been incredible.
The response from our research community has been phenomenal. Many hundreds of users, tens of thousands of designs and amazing feedback flowing in.
Jonathan Crabbé retweeted
🧬 Latent-X is our first frontier model for protein design.
For mini-binders, our AI model achieved picomolar binding affinities — the strongest reported vs prior methods in head-to-head lab validation.
Explore our best binders here (no login required): platform.latentlabs.com/
More details on the breakthrough in this🧵
Jonathan Crabbé retweeted
5 months from funding to shipping our first frontier model. Latent-X achieves state-of-the-art hit rates for macrocycles and mini-binders, with picomolar binding affinities — a breakthrough in de novo protein binder design.
Available now on our no-code platform!
Really happy to be releasing our first model on a beautiful platform. It generates de novo binders with impressive affinity and speed. If you are curious, you can try it for yourself, more info on the thread👇
Introducing Latent-X — our all-atom frontier AI model for protein binder design.
State-of-the-art lab performance, widely accessible via the Latent Labs Platform.
Free tier: platform.latentlabs.com
Blog: latentlabs.com/latent-x/
Technical report: tinyurl.com/latent-X
Jonathan Crabbé retweeted
Introducing Latent-X — our all-atom frontier AI model for protein binder design.
State-of-the-art lab performance, widely accessible via the Latent Labs Platform.
Free tier: platform.latentlabs.com
Blog: latentlabs.com/latent-x/
Technical report: tinyurl.com/latent-X
Jonathan Crabbé retweeted
@Latent_Labs comes out of stealth today with $50M funding. Our goal? To push the frontiers of generative biology, giving partners instant access to tools capable of accelerating drug design.
Every biotech or pharma company searching for the best therapeutic molecules understands the role AI can play - but not all are in a position to develop their own advanced models. That’s where @Latent_Labs comes in.
Jonathan Crabbé retweeted
Excited by the generality of CLIP, but need more fine-grained details in your representation? Introducing SPARC, a simple and scalable method for pretraining multimodal representations with fine-grained information. 🥳 1/6
Jonathan Crabbé retweeted
I'm very excited to share our papers that have been accepted at @aistats_conf2024! This year, we keep pushing our research on reality-centric AI - from advancing scientific discovery and adaptive experimental design to data-centric AI. Some amazing technical work by my students!
Jonathan Crabbé retweeted
Un rêve de pouvoir enfin porter les couleurs de la @KarmineCorp au plus haut niveau et d'écrire un nouveau chapitre pour l'organisation.
Rendez-vous dans 10 jours #KCORP
L'évasion s'est déroulée avec succès. 🕵️
Introducing our team for the 2024 #LEC Season :
🇫🇷 @CabochardLoL
🇨🇳 @zyblol
🇫🇷 @Saken_lol
🇩🇪 @LoL_Upset
🇧🇪 @Targamas
Présenté par @Orange_France 🟧
Always a pleasure to engage with the ML community at conferences! Had an amazing time at #NeurIPS2023 and really interesting conversations with researchers from all around the world. Really worth arguing with Reviewer 2 🤩
Jonathan Crabbé retweeted
Had an amazing time at #NeurIPS where we discovered that if you forget to take your poster down you may end up doing a whole second poster session the following morning feat. @JonathanICrabbe with an (appropriately?!) guilty expression 😅
Jonathan Crabbé retweeted
Wednesday at #NeurIPS2023! Today we are presenting posters with Yuchao Qin, Changhee Lee, @Jeffaresalan, @tennisonliu, and @JonathanICrabbe! Join us at the session to discusse Timely Outcome Prediction, Deep Ensemble Training, and everything #realitycentricAI!
Jonathan Crabbé retweeted
🚨Tomorrow (Wednesday) at 5pm #NeurIPS 🚨
If you are interested in ensembles, MoEs, optimisation, vision, or memes come visit myself & @JonathanICrabbe at poster #537!
Have we been training deep ensembles with the wrong objective? 😱
Our new #NeurIPS paper investigates why training ensembles *jointly* is almost never observed in practice and uncovers some pretty surprising behaviour…
🧵 [1/N]
🚨 𝐏𝐨𝐬𝐭𝐞𝐫 #𝟓𝟑𝟕 𝐖𝐞𝐝𝐧𝐞𝐬𝐝𝐚𝐲 𝟓𝐩𝐦-𝟕𝐩𝐦 🚨
We will challenge many intuitions on training deep ensembles at #NeurIPS2023. Super excited about this awesome collaboration between @Jeffaresalan, @tennisonliu, @MihaelaVDS and myself. Come chat with us & check the 🧵
Have we been training deep ensembles with the wrong objective? 😱
Our new #NeurIPS paper investigates why training ensembles *jointly* is almost never observed in practice and uncovers some pretty surprising behaviour…
🧵 [1/N]