Launch HN: BlankBio (YC S25) - Making RNA Programmable
BlankBio started from our PhD work in this area, which is open-sourced. There’s a model [2] and a benchmark with APIs access [0].
mRNA has the potential to encode vaccines, gene therapies, and cancer treatments. Yet designing effective mRNA remains a bottleneck. Today, scientists design mRNA by manually editing sequences AUGCGUAC... and testing the results through trial and error. It's like writing assembly code and managing individual memory addresses. The field is flooded with capital aimed at therapeutics companies: Strand ($153M), Orna ($221M), Sail Biomedicines ($440M) but the tooling to approach these problems remains low-level. That’s what we’re aiming to solve.
The big problem is that mRNA sequences are incomprehensible. They encode properties like half-life (how long RNA survives in cells) and translation efficiency (protein output), but we don't know how to optimize them. To get effective treatments, we need more precision. Scientists need sequences that target specific cell types to reduce dosage and side effects.
We envision a future where RNA designers operate at a higher level of abstraction. Imagine code like this:
seq = "AUGCAUGCAUGC..."
seq = BB.half_life(seq, target="6 hours")
seq = BB.cell_type(seq, target="hepatocytes")
seq = BB.expression(seq, level="high")
Using joint embedding architecture approaches (contrastive learning), we trained model to recognize functionally similar sequences rather than predict every nucleotide. This worked remarkably well. Our 10M parameter model, Orthrus, trained on 4 GPUs for 14 hours, beats Evo2, a 40B parameter model trained on 1000 GPUs for a month [0]. On mRNA half-life prediction, just by fitting a linear regression on our embeddings, we outperform supervised models. This work done during our academic days is the foundation for what we're building. We're improving training algorithms, growing the pre-training dataset, and making use of parameter scaling with the goal of designing effective mRNA therapeutics.
We have a lot to say about why other SSL approaches work better than next-token prediction and masked language modeling: some of which you can check out in Ian's blog post [1] and our paper [2]. The big takeaway is that the current approaches of applying NLP to scaling models for biological sequences won't get us all the way there. 90% of the genome can mutate without affecting fitness so training models to predict this noisy sequence results in suboptimal embeddings [3].
We think there are strong parallels between the digital and RNA revolutions. In the early days of computing, programmers wrote assembly code, managing registers and memory addresses directly. Today's RNA designers are manually tweaking sequences, improving stability or reduce immunogenicity through trial and error. As compilers freed programmers from low-level details, we're building the abstraction layer for RNA.
We currently have pilots with a few early stage biotechs proving out utility of our embeddings and our open source model is used by folks at Sanofi & GSK. We're looking for: (1) partners working on RNA adjacent modalities (2) feedback from anyone who's tried to design RNA sequences what were your pain points?, and (3) Ideas for other applications! We chatted with some biomarker providing companies, and some preliminary analyses demonstrate improved stratification.
Thanks for reading. Happy to answer questions about the technical approach, why genomics is different from language, or anything else.
- Phil, Ian, and Jonny
founders@blankbio.com
[0] mRNABench: https://www.biorxiv.org/content/10.1101/2025.07.05.662870v1
[1] Ian’s Blog on Scaling: https://quietflamingo.substack.com/p/scaling-is-dead-long-li...
[2] Orthrus: https://www.biorxiv.org/content/10.1101/2024.10.10.617658v3
[3] Zoonomia: https://www.science.org/doi/10.1126/science.abn3943
I have to admit, at a _glance_ this feels like a promising idea with few results and lots of marketing. I'll try to be clear about my confusion, feel free to explain if I'm off base.
- There's not a lot of talk of your "ground truth" for evaluations. Are you using mRNABench?
- Has you mRNABench paper been peer reviewed? You linked a preprint. (I know paper submission can be touch or stressful, and it's a superficial metric to be judged on!)
- Do any of your results suggest that this foundation model might be any good on out of sequence mRNA sequences? If not, then is the (current) model supposed to predict properties of natural mRNA sequences rather than of synthetic mRNA sequences?
- Did a lot mRNA sequences have experimental verification of their predicted properties? At a quick glance, I see this 66 number in the paper---but I truly have no idea.
I'm super happy to praise both incremental progress and putting forth a vision, I just also want to have a clear understanding of the current state-of-the-art as well!
Hey yes, the ground truth for our evaluations is measured experimental data. Our models are benchmarked using mRNABench, which aggregates results from high-throughput wet lab experiments.
Our goal, however, is to move beyond predicting existing experimental outcomes. We intend to design novel sequences and validate their function in our own lab. At that stage, the functional success of the RNA we design will become the ground truth.
> peer reviewed?
Both mRNA bench and Orthrus are in submission (at a big ML conference and a big name journal) - unfortunately the academic systems move slow but we're working on getting them out there.
> synthetic mRNA sequences
I think you're asking on generalizing out of distribution to unnatural sequences. There are two ways that we do this: (1) There are these screens called Massively Parallel Reporter Assays (MPRAs) and we eval for example on https://pubmed.ncbi.nlm.nih.gov/31267113/
Here all the sequences are synthetic and randomly designed and we do observe generalization. Ultimately it depends on the problem that we're tackling: some tasks like gene therapy design require endogenous sequences.
(2) The other angle is variant effect prediction (VEP). It can be thought of as a counterfactual prediction problem where you ask the model whether a small change in the input predicts a large change in the output. This is a good example of the study (https://www.biorxiv.org/content/10.1101/2025.02.11.637758v2)
> experimental verification of their predicted properties
all our model evaluations are predictions of experimental results! The datasets we use are collections of wet lab measurements, so the model is constantly benchmarked against ground-truth biology.
The evaluation method involves fitting a linear probe on the model's learned embeddings to predict the experimental signal. This directly tests whether the model's learned representation of an RNA sequence contains a linear combination of features that can predict its measured biological properties.
Thanks for the feedback I understand the caution around pre-prints. We believe a self-supervised learning approach is well-suited for this problem because it allows the model to first learn patterns from millions of unlabeled sequences before being fine-tuned on specific, and often smaller, experimental datasets.
I'm curious about what your strategy is for data collection to fuel improved algorithmic design. Are you building out experimental capacity to generate datasets in house, or is that largely farmed out to partners?
- mRNA therapies: These therapies deliver a synthetically created messenger RNA (mRNA) molecule, typically protected within a lipid nanoparticle (LNP), to a patient's cells. The cell's own machinery then uses this mRNA as a temporary blueprint to produce a specific protein.
The big example here is CAR-T therapy from Capstan which just got acquired for 2.1B. Their asset,CPTX2309 , is currently in Phase 1. Previously to do Car-T therapy you had to extract a patient's T-cells and genetically engineer them in a special facility. Now the mRNA gets delivered directly to the patient's t cells which significantly lowers the cost and technical hurdles.
- RNA interferences (RNAi): Used for gene expression knockdown through natural cellular mechanisms for viral detection. The big example here is Alnylam with 5 approved therapies and a number in clinical trials.
- Antisense Oligonucleotides (ASOs): Short single stranded RNA molecules that get delivered directly to the cell and target an existing mRNA. The big win here is Spinraza which is the first approved treatment for Spinal Muscular Atrophy (SMA) which previously didn't have a treatment. The Spinraza clinical trial (ENDEAR) was so effective that they deemed it unethical to continue it because the control arm wasn't receiving the treatment. Prior to Spinraza most patients would pass away prior to two years of age.
How would your AI solution help with finding natural analogs of or alternatives to or foils of mRNA procedures?
Re: "Sensitization of tumours to immunotherapy by boosting early type-I interferon responses enables epitope spreading" (2025) https://www.nature.com/articles/s41551-025-01380-1
How is this relevant to mRNA vaccines?:
"Ocean Sugar Makes Cancer Cells Explode" (2025) https://scitechdaily.com/ocean-sugar-makes-cancer-cells-expl... ... “A Novel Exopolysaccharide, Highly Prevalent in Marine Spongiibacter, Triggers Pyroptosis to Exhibit Potent Anticancer Effects” (2025) DOI: 10.1096/fj.202500412R https://faseb.onlinelibrary.wiley.com/doi/10.1096/fj.2025004...