Eli Weinstein

Eli Weinstein

@eliweinstein.bsky.social

Assistant professor of chemistry at the Technical University of Denmark (DTU). Also at Jura Bio. machine learning, statistics, chemistry, biophysics https://eweinstein.github.io/

702 followers198 following27 posts

Eli Weinstein reposted

Replying to @kkj15.bsky.social

We further validated LIDS on protein language model (PLM) oracles in silico. It is very fast. Here, LIDS can find the optimum in 2 experimental rounds (median), while individual synthesis alternatives need an order of magnitude more experiments.

Very excited for my first PhD student’s first preprint. We’re bringing generative model-controlled stochastic synthesis beyond DNA and into new areas of chemistry, without the need for barcoding or sequencing-based readouts.

Kasper Krunderup Jakobsen@kkj15.bsky.social

How do lab-in-the-loop ML systems learn most efficiently? How can you pack as much information as possible into an experiment? We explored this in our new preprint “Information-Dense Synthesis for Molecular Discovery” (arxiv.org/abs/2610.08495).

Eli Weinstein reposted

On model ablations: Linear models plateau on this data (dashed), even with foundation model representations (colors). We needed big transformers (solid) to scale.

Elizabeth Wood, PhD@lizbwood.bsky.social

Can we achieve the same scaling laws in biological AI as we have in the rest of machine learning? We found that @jura.bsky.social's mix of designed data generation and training produce robust scaling laws: more data reliably lead to better predictions over many orders of magnitude.

Very excited about our new results, showing variational synthesis + large scale screens + co-designed training -> robust scaling laws for sequence-activity models.

Elizabeth Wood, PhD@lizbwood.bsky.social

Can we achieve the same scaling laws in biological AI as we have in the rest of machine learning? We found that @jura.bsky.social's mix of designed data generation and training produce robust scaling laws: more data reliably lead to better predictions over many orders of magnitude.

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This causal approach lets us derive novel equivariant estimators for the effects of mutations that combine these different models in different ways, drawing on advances in CATE estimation.

Overall, I am excited about geometric causal models' potential for bringing causal machine learning methods and ideas into new areas of science, including especially the molecular sciences where symmetries abound.

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We illustrate with a causal model for functional genomics that satisfies the symmetries of DNA. Methods such as AlphaGenome emerge as outcome models, while generative and masked DNA language models are the corresponding propensity models.

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Classical causal methods assume data is iid, but this is often false in practice. Instead, we posit the data satisfies more general symmetries, specified in the language of group theory.

These new geometric causal models describe causal relationships using equivariant maps, and can be used to draw causal inferences from complex scientific data, including spatial, graph, and molecular data.

Eli Weinstein reposted

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

Elizabeth Wood, PhD@lizbwood.bsky.social

JURA is launching a new target intelligence dashboard called MESA jurabio.com/mesa with a nice interactive case study from our recent TCRm campaigns. Go check it out -- we'll open to crowd-sourced targets soon. @jura.bsky.social

Eli Weinstein reposted

Replying to @lizbwood.bsky.social

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...

jurabio.comOne 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...

Eli Weinstein reposted

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...

Are you looking for a PhD? Join us in Barcelona! You'll dive into a community of >100 PhD students from 30 countries exploring the frontiers of biology. You can also join an online workshop on 6 November (15:00 CET) to learn how to find the right lab for you. More info: www.crg.eu/en/content/t...

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Eli Weinstein reposted

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.

Nature@nature.com

BREAKING: The Nobel Prize in Physiology or Medicine has been awarded jointly to Mary E. Brunkow, Fred Ramsdell, and Shimon Sakaguchi "for their discoveries concerning peripheral immune tolerance" Stay tuned for more. #NobelPrize

Eli Weinstein reposted

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.

Elizabeth Wood, PhD@lizbwood.bsky.social

The biggest challenge for AI in biology isn't just models, it's the data used to train them. Standard biological data isn't built for AI. To unlock generative AI for drug discovery, we must rethink how we generate and capture data. 1/

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.

Eli Weinstein reposted

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! 🧵:

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