variational synthesis is now published, with the addition of large functional studies. we've now scaled the assays further, to train massive sequence-activity models www.jurabio.com/blog/scaling...

Ever wondered what it'd be like to run a de novo antibody campaign against 100 of the hardest targets, simultaneously, with 76% success rate, and have the complete wetlab validated results days from the project's start? We show you what that looks like, too: www.jurabio.com/mesa

One billion simultaneous experiments — JURA Bio, Inc. For decades, drug discovery has been constrained by a simple fact: experiments are expensive, so you have to guess well. We built a system where you don't have to guess — testing a billion distinct m...

Prediction is overhead when verification is cheap.

We've spent years building a system where verification is cheap -- generationally so.

This changes the logic of discovery in ways that are easy to underestimate. I tried to write a little about what that means: www.jurabio.com/blog/onebill...

We are at the #StartupVillage at #EurIPS today — come say hi 👋

@jura.bsky.social

Today we're releasing a technical blogpost on LIFT, a system that increases the information density of wetlab experiments by orders of magnitude without requiring more cells, more reagents, or more sequencing.

Applications are open for the @crg_eu PhD Programme! 20 fully funded positions — including one in our group through the Evolutionary Medical Genomics ITN.

Join us to develop deep generative models of cross-species data to tackle open questions in disease genetics.

www.crg.eu/en/content/t...

Accelerated Learning on Large Scale Screens using Generative Library Models Biological machine learning is often bottlenecked by a lack of scaled data. One promising route to relieving data bottlenecks is through high throughput screens, which can experimentally test the acti...

Paper: arxiv.org/abs/2510.16612
Blog: www.jura.bio/blog/leavs
Team: @lizbwood.bsky.social @highvariance.bsky.social @mgollub.bsky.social

We demonstrate empirically and prove theoretically that LeaVS can dramatically accelerate learning, increasing the effective dataset size by orders of magnitude.

Crucially, it depends on jointly modifying the experimental protocol and the training algorithm: on their own, neither modification helps.

This approach lets you focus limited measurements on the most informative datapoints, maximizing information gain without compromising reliability.

Second, modify the training algorithm: compensate for the missing negatives by incorporating the generative variational synthesis model into the objective.

First, modify the experiment: only measure positive examples of functional proteins. Don't spend a limited sequencing budget on any negatives.

In this paper we describe a method to overcome this measurement bottleneck.

To test, we can deliver billions of designs to different cells. But there is a cost to recovering those designs' function, to obtain (x,y) data.

With variational synthesis, we can now build quadrillions of generative model-designed sequences. The bottleneck is now testing, not synthesis.

Scaling up protein ML requires understanding and eliminating bottlenecks in the design-build-test-learn cycle.

We're excited to present LeaVS, a method to scale up learning for protein function models. It is based on the co-design of wet lab experiments and in silico training.

For every Nobel that goes to a criminally under-recognized woman scientist (Brunkow, Karikó), or fails to go (Candy Lee), a week of mourning and reform for an academic system wherein you can do Nobel-prize-worthy-work and still end up without a conceivable path to being a professor.

LeaVS: Accelerating learning for biological AI — JURA Bio, Inc. A fundamental lesson of modern AI is that scale is essential: training bigger models on bigger datasets unlocks new capabilities. A fundamental lesson of AI engineering is that scaling up isn't trivia...

You can read more in our post at www.jurabio.com/blog/leavs; preprint forthcoming.

@jura.bsky.social @eliweinstein.bsky.social @mgollub.bsky.social @highvariance.bsky.social

PhD scholarship in Machine Learning for Molecules - DTU Chemistry Advance large scale data generation for chemical and biological AI in a 3 year PhD. Work on the frontier of active learning, and develop novel probabilistic machine learning techniques for experiment ...

I'm looking for my first PhD student! We will push the frontiers of probabilistic machine learning for the molecular sciences, and study how to design new algorithms that exploit the unique properties of molecular systems to learn about the world.

efzu.fa.em2.oraclecloud.com/hcmUI/Candid...

So excited about this - we did iterative generative design at large scale with variational synthesis, and got human scFv candidates against some of the hardest therapeutic targets around.

🔊 The call for the first round of open PhD fellowships from the newly formed Danish Advanced Research Academy (DARA) has just been announced:

daracademy.dk/fellowship/f...

Exceptional candidates with a strong background in theoretical chemistry are more than welcome to reach out to me for support.

We can make population genetics studies more powerful by building priors of variant effect size from features like binding. But we’ve been stuck on linear models! We introduce DeepWAS to learn deep priors on millions of variants! #ICML2025 Andres Potapczynski, @andrewgwils.bsky.social 1/7

If you are interested in working with me as a student or postdoc, or otherwise collaborating, please reach out.

Thrilled to announce that I am joining DTU in Copenhagen in the fall, as an assistant professor of chemistry.

My research group will focus on fundamental methodology in machine learning for molecules.

The BayesComp workshop on 'Bayesian Computation and Inference with Misspecified Models' will take place in Singapore on the 16-17th June.

We have an open call for posters/contributed calls, with a deadline on the 1st May. More details on the website:
postbayes.github.io/BayesMisspec...

Calendar - Machine Learning - CMU - Carnegie Mellon University Calendar of the Machine Learning Department at Carnegie Mellon University

This Thursday, 1PM, @eweinstein.bsky.social is headed back to CMU, this time to the Machine Learning Department. He'll be sharing his work on hierarchical causal models. If you're on campus, I encourage to you check it out!

www.ml.cmu.edu/calendar/

Tomorrow -- another chance to learn about variational synthesis, probabilistic experimental design methods, & ways to capture scaled functional data for model training: this time with zoom access for those of you remote, 1pm at @cmu.edu Computer Science. Details at:

www.cs.cmu.edu/calendar/181...

Today at UPenn's CIS Seminar: a chance to hear about variational synthesis, probabilistic experimental design methods, and ways to capture modern ML-scale functional data for biology: events.seas.upenn.edu/event/13596/

1/n I'm thrilled to share *Deep generative modelling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders* t.co/DoN4DrSFbS from a wonderful collaboration between the Marks Lab and Dias and Frazer group, with @roseorenbuch as lead! 🧵: