Ian Quigley @allmeasures
Cofounder, @LeashBio. Ex-Recursion, Arima Genomics, Nanocellect, Salk, UT Austin, Baylor College of Medicine, Rice. He/him. Salt Lake City, UT Joined May 2010-
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1/ Over the past 50 years, we have learned much about how transcription factors and other DNA-binding proteins search for their targets, but we know much less about how sequence-specific RNA-binding proteins (RBPs) search for their targets. 🧵⤵️
me: hey can I get a 5 ultra high definition photos of 70ft tall Anne Hathaway stomping around Manhattan and crushing people under red heels? midjourney: sure here you go me: btw do you know where I can go to get cancer screenings? midjourney: you’re not gonna believe this
You love to see it
We are releasing a blog post to highlight some of the challenges in classifying binders from denovo design models and to propose a more rigorous evaluation protocol from what we often observe in the field (including our own past work)! boltz.bio/experimental-p…
@mihailoxyz @boltz_bio We found that chemist (molecule creator) splits are probably better than tanimoto distance arxiv.org/abs/2512.20924
👏👏👏
Big news from Boltz - our biggest update yet! 🚀 Today we’re releasing two new state-of-the-art models for protein and small molecule design with extensive wet lab validation and a new API to run all of our models on scalable GPUs wherever you (or your agents) work! 🔥
Here is reviewer #3's critique of our work which we address in the replies. This is important work. Response prediction in real clinical data is hard precisely because the cohorts are small. Our team is bringing a ton of rigor to this work, so it will be useful for patients.
Overclaiming based on one cohort & - no comparison to other models than do what they did - H&E to bulk-expression to survival. -Random thresholding to create survival groups (suggests me that the whole survival results is BS) Extremely over-claiming thread.
New paper! How do RNAs "know" where to go inside a cell? We dug into the sequence elements that route RNAs to the right place. It turns out that, in mammals, they're surprisingly massive (>200 nt), multipartite, and wonderfully complicated. 🧵
🌱 504k public plant RNA-seq samples × 1,200+ species → live interactive UMAP atlas. Click any organ (256 PO terms) or evo-devo trait (57 of them) → top gene families upregulated pop up on the right. huggingface.co/spaces/mutwil/…
For the first time, most biotech deal value now goes to assets from China:
No scaling laws for single-cell foundation models: when bigger atlases stop teaching the model anything In language and vision, the recipe has been simple: more data, bigger models, better performance. Single-cell biology borrowed that playbook. Foundation models for transcriptomics jumped from 1 million cells to atlases of over 100 million, on the assumption that scale would unlock the same gains. Alan DenAdel and coauthors put that assumption to the test, and the result is sobering. Working from a 22.2-million-cell corpus, they pretrained 400 models across five architectures (from PCA and a variational autoencoder up to the Geneformer transformer) and ran 6,400 evaluation experiments. They varied not just dataset size (1% to 75%) but also diversity, using cell-type re-weighting and geometric sketching to deliberately enrich rare cell types and transcriptional states. The finding: performance saturates almost immediately. On cell-type classification, batch integration, and perturbation prediction, most models hit their ceiling at roughly 1% of the corpus, about 200,000 cells. Beyond that, adding millions more cells changed essentially nothing. More diversity didn't help. Even spiking in genome-scale Perturb-seq data, to give the models perturbed phenotypes rather than just healthy ones, failed to move the needle. Larger models did score better overall, but they too plateaued early on data. Two points stood out. Simple baselines (PCA, logistic regression) often matched or beat the transformers. And the strongest model, SCimilarity, won not because of size but because its contrastive training objective is aligned with the downstream task. For single-cell data, what you train on and how you frame the objective matters far more than how much you collect. This reframes a quiet but expensive habit. In drug discovery, biotech, and any pipeline leaning on cell atlases, the instinct to keep scaling pretraining corpora may be burning compute for no return. The real leverage sits elsewhere: curating high-quality, task-relevant data and matching the training objective to the actual question you're trying to answer. Paper: DenAdel et al., journal license | doi.org/10.1038/s41592…
its a very small community in ai bio, everyone knows each other. we are working on human health, integrity is a big deal.
I'm on a mission to make this plot as publicly recognizable as the one with the holes in the WW2 airplane
Done.
I'm on a mission to make this plot as publicly recognizable as the one with the holes in the WW2 airplane
Big funding agencies / VCs / companies etc interested in powering true breakthroughs in AI for biology should fund tech dev efforts that can produce & scale the right types of data to push models out of zones they struggle in. 8/
I like this but the Takifugu rubripes genome is ~1/8 as big as ours, has about the same number of genes, has bones gut neurons etc, maybe start there?
How much of the human genome is essential? Two pieces out today from our lab: 1) a method to map essential genomic intervals at gigabase scale, and 2) an argument that it's time to consider synthesizing a minimal human genome. biorxiv.org/content/10.648… nature.com/articles/d4158…
Most experiments fail, and negative results rarely get published. This means LLMs are unaware of the outcomes of most experiments.
@bauer_lesavage @_DimensionCap Thanks for the Clever Hans shoutout!
Another great research piece by @_DimensionCap @bauer_lesavage on Training Data for Bio AI. Models will only be as good as the underlying data, and the biology they learn will be constrained by the limitations of that data. We need to think deeply about scaling the best quality data to solve human bio with AI. Our thesis from day 1 at @NOETIK_ai.
The training data market has exploded for LLMs and bio foundation models are next. But biological data is extremely complex and requires a data generation playbook that prioritizes quality over immediate scale. @_DimensionCap Research article live now! research.dimensioncap.com/p/on-training-…
Probably noisy as hell but if you were smart about the chemical library design .. so crazy it just might work
Somebody needs to do this and then throw a DEL at it and do in situ sequencing of the DEL barcodes. You could get cellular chemistry tropism at scale
Hello world, meet 1,000× Expansion Microscopy. 1,000,000,000× expansion by volume! A gel that starts at a few centimeters will then expand to the volume of an Olympic swimming pool. biorxiv.org/content/10.648… In our new bioRxiv preprint, work carried out between MIT and UMG, led by
Recursion @RecursionPharma
17K Followers 769 Following Decoding biology to radically improve lives. The industrial revolution of drug discovery is here.
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