Launch HN: BlankBio (YC S25) - Making RNA Programmable

Summary

Hey HN, we're Phil, Ian and Jonny, and we're building BlankBio (https://blank.bio). We're training RNA foundation models to power a computational toolkit for therapeutics. The first application is in mRNA design where our vision is for any biologist to design an effective therapeutic sequence (https://www.youtube.com/watch?v=ZgI7WJ1SygI).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") To get there we need generalizable RNA embeddings from pre-trained models. During our PhDs, Ian and I worked on self-supervised learning (SSL) objectives for RNA. This approach allows us to train on unlabeled data and has advantages: (1) we don't require noisy experimental data, and (2) the amount of unlabeled data is significantly greater than labeled. However ...