BIOS (@biosdiscovery)

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RFD4-Proteina now runs locally on a MacBook.

Introducing BIOS’s Mac port: generate candidate protein binders and screen their predicted structures locally.

Built with MLX and custom Metal kernels.

• 2.2× faster generation than stock PyTorch-MPS at its default precision
• 20 designs generated and screened → 10 ranked candidates in about 12 minutes on GLP-1R (M5 Pro)

We believe more researchers should be able to run AI experiments on their own hardware. Test ideas locally, keep your research private, and scale to NVIDIA GPUs when you’re ready. After setup, the workflow runs offline.

Watch it run below. ↓

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BIOS @biosdiscovery Sep 19

More researchers should be able to help design potential drug candidates.

Anthropic × Adaptyv’s protein design competition is an invitation to do that.

It’s why we’re building BIOS: to help researchers design and shortlist computational binders for testing in the lab.

Adaptyv Bio Verified account @adaptyvbio Sep 17

We’re partnering with @Anthropic to launch the biggest Protein Design Competition in the world, challenging people around the world to use AI to design new potential drug candidates for diseases that affect millions of lives.

The competition will feature five challenges, each focused on a specific disease or biological mechanism. Compared to previous competitions, it will be a big step-up in complexity and scale to push the boundaries of AI-driven protein design.

Together with Anthropic, we’re sponsoring over $1 million in experimental validation, making it possible to test more than 5,000 protein designs in our automated lab at no cost to participants. Anthropic is providing an additional $1 million in Claude credits.

All experimental results will be published openly on @Proteinbase, including designs that didn’t work, so anyone can access the data and build on what we learn.

The competition is open to everyone and free to enter. It will feature 3 tracks:

- Track 1 is aimed at expert protein designers, with up to 20 teams to be selected.

- Track 2 is targeting life science academics and industry researchers.

- Track 3 is open to everyone from tech enthusiasts to high-school students.

By combining Anthropic’s models with access to our automated lab, we want to make it possible for anyone with a laptop and an internet connection to join the global effort to advance human health with AI.

A big thanks to @Modal for contributing compute for protein design and to @TwistBioscience for contributing the DNA for the experimental validation!

Sign up link below -

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BIOS @biosdiscovery Aug 26

🫡

Sam Rodriques Verified account @SGRodriques Aug 26

BixBench3 is out! Our latest eval for biology capabilities in language models. Essentially requires agents to reproduce all work needed to write a paper from raw data. OpenAI is in the lead with 5.6-Sol at 48%, Anthropic is in 4th with Opus 4.8, after Kimi and GLM. Opus 5 has weird instruction following problems that severely degrade its performance. I’m sure Fable is great but can't be evalled on this. Read more below.

Also lest we miss the forest for the trees, we’re in a position now where the models can essentially reproduce entire papers ~50% of the time. Crazy time to be alive.

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BIOS @biosdiscovery Aug 18

The demand for automated wet-lab validation will increase significantly.

NVIDIA Healthcare Verified account @NVIDIAHealth Aug 18

Using NVIDIA Proteina-Complexa, @muni_bio's autoresearch agent explored nearly 14,000 protein designs.

@adaptyvbio validated nine TREM2 binders, three with sub-nanomolar affinity.

The results show how agents can connect computational design with wet-lab feedback to improve the next round of discovery. 📘muni.bio/research/closi…

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BIOS @biosdiscovery Aug 13

Why scientists should lead the shift away from AI mega data centres?

The article by @stokel for @Nature makes the case for publicly available models and local infrastructure that give researchers more control over the tools they use.

That focus on access and researcher control also sits behind our work on BIOS: opening the binder-design workflow so researchers can run it themselves, from target structure to ranked candidates.

nature Verified account @Nature Aug 7

An AI model has revealed that some trusted numbers in one reference database have been wrong all along

go.nature.com/45KjsIe
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