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    Claude optimizations reportedly make more than 30 open-source biology models 4x faster on average

    Anthropic credits custom GPU software with part of the speedup and says it is open-sourcing all the optimization code. It also announced a protein design competition with Adaptyv Bio, with plans to experimentally validate more than 5,000 designs.

    Kyunghyun ChoKC
    AnthropicAN
    Patrick KidgerPK
    9 Sources, ,

    TLDR

    Anthropic says Claude optimized inference—the process of running a model—for more than 30 open-source biology models, making them 4x faster on average. These models handle tasks such as modeling molecular structures, designing drug-like molecules and predicting genetic mutations’ effects. The company says custom GPU software contributed to the gains and is open-sourcing all the optimization code.

    Anthropic also announced a protein design competition with Adaptyv Bio, saying they will experimentally validate more than 5,000 designs. The announced support includes up to $1 million in Claude credits from Anthropic, plus validation funding from Anthropic and Adaptyv. Modal is contributing up to $250,000 in compute, and Twist Bioscience is providing DNA.

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    9 Sources

    Anthropic@AnthropicAIBiologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-like molecules, and predicting the effects of genetic mutations. But these models are often expensive to run, potentially limiting their impact. In our latest Science Blog, we share how Claude was able to optimize inference for more than 30 open-source models, making them 4x faster on average, partly by writing custom software for GPUs. We’re open sourcing all of the optimization code. Read more: https://www.anthropic.com/research/claude-uplifts-biomolecular-modeling20d
    Adaptyv Bio@adaptyvbioWe’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 -20d
    Ellen Zhong@ZhongingAlongRT @AnthropicAI: Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-l…20d
    Patrick Kidger@PatrickKidgerRT @adaptyvbio: We’re partnering with @Anthropic to launch the biggest Protein Design Competition in the world, challenging people around t…19d
    Nathan C. Frey@nc_freyWe optimized over 30 open-source models for structure prediction and molecular design, making them 4x faster on average using an internal research model here at @AnthropicAI. We also created a low-memory "Big" mode that allows structure prediction for molecular machines larger than 10,000 amino acids on a single GPU. All the optimized code is open-sourced. Claude can now use these optimized models to achieve state-of-the-art molecule design results with a 100x reduction in GPU hours needed. Models like AlphaFold3, OpenFold3, and Boltz-2 spend much of their computation on triangle attention and triangle multiplication, which are cubic in runtime and memory. We developed FlashPairformer with Claude, achieving a new state-of-the-art speedup of 2.7-2.9x on triangle attention and 1.7-3.2x on triangle multiplication compared to baselines. We used "Big" mode to fold human mitochondrial complex I, the TRiC chaperone complex, a proteasome, and a bacterial ribosome, each closely matching its experimentally determined structure. To our knowledge, these are the largest structures ever folded accurately using structure prediction models. Claude also folded an entire protein compartment using a single 8-GPU node.19d
    Kyunghyun Cho@kchonycRT @nc_frey: We optimized over 30 open-source models for structure prediction and molecular design, making them 4x faster on average using…19d
    Bharath Ramsundar@rbhar90RT @AnthropicAI: Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-l…19d

    9 Sources

    Anthropic@AnthropicAIBiologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-like molecules, and predicting the effects of genetic mutations. But these models are often expensive to run, potentially limiting their impact. In our latest Science Blog, we share how Claude was able to optimize inference for more than 30 open-source models, making them 4x faster on average, partly by writing custom software for GPUs. We’re open sourcing all of the optimization code. Read more: https://www.anthropic.com/research/claude-uplifts-biomolecular-modeling20d
    Adaptyv Bio@adaptyvbioWe’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 -20d
    Ellen Zhong@ZhongingAlongRT @AnthropicAI: Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-l…20d
    Patrick Kidger@PatrickKidgerRT @adaptyvbio: We’re partnering with @Anthropic to launch the biggest Protein Design Competition in the world, challenging people around t…19d
    Nathan C. Frey@nc_freyWe optimized over 30 open-source models for structure prediction and molecular design, making them 4x faster on average using an internal research model here at @AnthropicAI. We also created a low-memory "Big" mode that allows structure prediction for molecular machines larger than 10,000 amino acids on a single GPU. All the optimized code is open-sourced. Claude can now use these optimized models to achieve state-of-the-art molecule design results with a 100x reduction in GPU hours needed. Models like AlphaFold3, OpenFold3, and Boltz-2 spend much of their computation on triangle attention and triangle multiplication, which are cubic in runtime and memory. We developed FlashPairformer with Claude, achieving a new state-of-the-art speedup of 2.7-2.9x on triangle attention and 1.7-3.2x on triangle multiplication compared to baselines. We used "Big" mode to fold human mitochondrial complex I, the TRiC chaperone complex, a proteasome, and a bacterial ribosome, each closely matching its experimentally determined structure. To our knowledge, these are the largest structures ever folded accurately using structure prediction models. Claude also folded an entire protein compartment using a single 8-GPU node.19d
    Kyunghyun Cho@kchonycRT @nc_frey: We optimized over 30 open-source models for structure prediction and molecular design, making them 4x faster on average using…19d
    Bharath Ramsundar@rbhar90RT @AnthropicAI: Biologists use specialized open-source models for tasks like modeling the structure of molecular systems, designing drug-l…19d