In Mercury’s early days, I called support because some onboarding button wasn’t working.
The support rep went quiet for two minutes, came back, and said, “Refresh the browser.”
It fixed my problem.
I said, “Wait, why are you doing support if you can code?”
He said, “I’m the CEO.” 🤯
Upcoming feature: @muni_bio uses dynamic workflows to power its autoresearch, chaining the best bio + chem tools/models to go after hard problems.
Unlocking the ai + bio pipeline for real-world problems has been our core mission these past few months.
Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology.
The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics.
We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity.
We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures.
ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences.
A world model of protein biology emerges through language modeling.
We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins.
The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science.
This understanding emerges without prior knowledge, just from language modeling of protein sequences.
Language models are becoming a powerful substrate to understand and program biology.
The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders.
I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine.
Congrats to @OpenAI and @xai for their scientific reasoning.
Last week we got the wet-lab results back from our TREM2 hackathon. 6 autonomous agents + 9 human teams designed TREM2 binders in a single day.
Agents nearly matched human hit rates.
read more: https://t.co/U8NCI58Qw7
@draparente@OpenAI@xai Muni’s inference provider is openrouter and it’s quite expensive to run opus and GPT pro for long running sessions. Now that we have the muni cli it would be more cost-effective to use something like opus4.7 or gpt5.5.
@BrantlyMillegan Isn't it funny? the guy who got kicked out of ENS because of his values, is one of the few solid devs that is still building in the space because of his...values