Krishna Yerramsetty @yerrkimo
Former chemical engineer. Working at the intersection of AI, Synthetic Biology and CRISPR. San Diego, CA Joined July 2014-
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Yes, this fact alone still blows my mind after 2 years
If you are overthinking, write. If you are underthinking, read.
1/4 🚀 Announcing the 2025 Protein Engineering Tournament. This year’s challenge: design PETase enzymes, which degrade the type of plastic in bottles. Can AI-guided protein design help solve the climate crisis? Let’s find out! ⬇️ #AIforBiology #ClimateTech #ProteinEngineering…
The exact response I got last year from a senior software engineer
James Cameron's Titanic was a side-quest from his desire to dive the ship-wreck and all of Tom Cruise's movies could be side-quests too and his main quest is finding out evermore insane ways to get adrenaline rush :)
a mistake I've made when starting a new job: when new, and you see a bunch of people pushing a rock, you don't try to figure out why the rock needs pushed, what the rock pushing strategy is, whether the rock is being pushed in the right direction you just run and help push
And Pat Symcox and Klusener. SA in the 90s were beastmode
And Pat Symcox and Klusener. SA in the 90s were beastmode
Maybe. The challenge is how do you evaluate that the newer architectures are better on noisy biological data? The reason LLMs and attention work well (?) for biological dataset IMO is because they already succeeded in natural language processing.
Maybe. The challenge is how do you evaluate that the newer architectures are better on noisy biological data? The reason LLMs and attention work well (?) for biological dataset IMO is because they already succeeded in natural language processing.
And also account for evolution. Life will find a way ;)
And also account for evolution. Life will find a way ;) https://t.co/7wrQwa1nrS
The challenge in biotech is the slow feedback cycle from idea to testing at scale. That's pretty much it I think. Everything will get exponentially faster once we unlock this faster feedback loop
The challenge in biotech is the slow feedback cycle from idea to testing at scale. That's pretty much it I think. Everything will get exponentially faster once we unlock this faster feedback loop
As you can probably tell from our work, we're now all in on diffusion and flow matching models for protein/peptide sequence generation! 🌟 These models are just way better for tightly controlling generation (vs. say autoregressive models, which we've found are poorly set up for…
It's hard to explain, I am not an expert on this. But uv just works
It's hard to explain, I am not an expert on this. But uv just works
Transformers and next token prediction are a step change but I feel like we are running out of better architecture ideas for biological data. I think it is back to "data is king" in biology. Unfortunately, too much emphasis still on building a "better" model
Just tried @coderabbitai for the first time. And it already saved me hours of pain by identifying that I was using the rotary embeddings on the query and value vectors instead of query and key vectors. Love it!
Trying to understand the "reasoning" or "chain-of-thought" LLMs and one thing that sticks out is how natural such approaches could be for protein and small molecule engineering/generation. The slowest part in the loop is still validation in the lab and we can only test a few…

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