How Claude is uplifting biomolecular modeling

來源原標題

How Claude is uplifting biomolecular modeling

機構

Anthropic

原始標籤

Research

原始連結

https://www.anthropic.com/research/claude-uplifts-biomolecular-modeling

摘要原文

Science How Claude is uplifting biomolecular modeling Sep 17, 2026 In this post, we share how Claude made the open-source models that scientists use to predict and design biomolecules faster and more memory-efficient. Claude optimized more than 30 of these models in just under four weeks, speeding them up roughly 4x on average. It also created a low-memory mode that enables the accurate prediction of biomolecular systems larger than 10,000 tokens (amino acids, nucleotides, and atoms from small molecules and ions) on a single NVIDIA GPU node. We are open-sourcing all of the optimized code and announcing a protein design competition co-sponsored with Adaptyv Bio, backed by up to $1 million in Claude credits and wet lab validation for over 5,000 designs. Recently, we shared results demonstrating Claude’s abilities to design de novo protein binders through expert-level orchestration of open-source protein design and structure prediction models. De novo binders are small, computationally designed proteins that attach tightly to a specific target molecule to activate, block, or deliver something to it. Although this was an encouraging demonstration of AI’s scientific capabilities and an early step towards advancing drug discovery, it took more resources than would be available to the vast majority of protein designers. We allowed Claude to spend up to $10,000 per target on the AI infrastructure platform Modal, roughly equivalent to 2,500 NVIDIA H100 GPU hours. To make such research more accessible, we began to explore inference optimizations to run these models more efficiently. As an early result of these optimizations, Claude Mythos 5.1 accelerated seven open-source biology models, enabling them to run up to 2.5 times faster. Here, we present new results showing how an internal, general-purpose research model was able to optimize more than 30 deep learning models trained for a variety of biological tasks, such as structure prediction and protein design, as well as for genomics and protein language models. On average, Claude was able to speed up such tasks roughly 4x while sacrificing a minimal amount of precision, and nearly 2x with identical outputs. Claude also improved the memory utilization of these models, making it possible to predict biomolecular systems of unprecedented sizes. By combining these results with simplifications to our previous agentic protein design approach, we show that Claude can achieve comparable in silico performance to the results we previously reported using two orders of magnitude fewer GPU hours. Beyond protein design, these specialized biological models are widely used by molecular biologists, including for drug discovery and development. We are open-sourcing the optimized code for all of these models today ( here ) so that the broader community can make use of them. You can find more detail in our technical report ( here ). To further support the community, we are also co-sponsoring a protein design competition with Adaptyv Bio, which has pioneered open protein design competitions . We’ve jointly selected five challenging problems at the frontier of today’s capabilities. Together with Adaptyv, and thanks to generous contributions from Modal and Twist Bioscience, we’re committing up to $1 million in Claude credits and $250,000 in Modal compute credits, as well as wet lab validation for over 5,000 designs. Find more information ( here ) and ( apply here ). Accelerating protein structure prediction and design models Protein structure prediction is the problem of determining the three-dimensional structure of a protein from its sequence of amino acids alone. Protein design, meanwhile, is the process of creating a protein with a specific structure, function, or set of properties. Together, these computational tools allow scientists to interrogate key biomolecular processes, such as how cancers form, and to create useful molecules, such as drugs that could target these cancers. Modern structure prediction models, such as AlphaFold3, OpenFold3, and Boltz-2, spend much of their computational runtime and memory on two operations: triangle attention and triangle multiplication, which act on triplets of tokens. These operations make it possible to model the geometry of biomolecular systems, but they are extremely computationally expensive, because they are cubic in both runtime and memory: doubling the size of the system uses 8x more time and memory, while tripling it uses 27x more. Writing kernels—low-level software translation layers for accelerated computing hardware such as GPUs—is a standard approach for reducing these costs. Given their significance, triangle attention and multiplication have been the subject of dedicated kernel development efforts, first with NVIDIA’s cuEquivariance and more recently with NVIDIA’s BioNeMo Inference Runtime (BioNeMo-IR). For our own effort to optimize inference for structure prediction models, we worked with Claude to develop FlashPairformer, a set of custom kernels that speed up triangle attention and multiplication. It achieves a new state-of-the-art, outperforming the field standard on average by 2.7-2.9x on triangle attention and 1.7-3.2x on triangle multiplication, depending on the model configuration. We worked with Claude to develop FlashPairformer, a set of custom kernels that accelerate triangle attention and multiplication, which are the main components of the Pairformer architecture that underlies state-of-the-art biomolecular structure prediction models. Results are reported relative to the field standard . In addition to developing transferable kernels, we pointed Claude at each individual model with the goal of producing more specific optimizations. These included changes like caching redundant recomputed work and simplifying dead branches into their constant outputs. The combination of these improvements accelerated the structure prediction models by 4x, on average, and for each model, we confirmed that Claude’s accelerated versions did not impact performance on the downstream task (such as structure prediction). It normally takes an experienced team of engineers weeks to produce such optimizations for each model, and the work often does not transfer between models. Claude, supervised by two members of Anthropic’s technical staff who are experienced in biomolecular modeling but who had no prior experience in inference optimization or kernel engineering, carried out the acceleration of more than 30 open-source models across biomolecular structure prediction, protein design, protein language modeling, and genomics in just under four weeks. Our results suggest that frontier AI models will help others in the field build scientific tools with greater speed and ease. Claude’s optimizations accelerated over a dozen biomolecular structure prediction models, achieving, on average, a roughly 4x speed-up with minimal decrease in precision and a roughly 1.6x speed-up with identical outputs. Note: ColabFold 1.6.3’s concurrently-released optional fast kernels are not yet benchmarked here. Claude’s optimizations also sped up multiple protein design models spanning hallucination, structure generation, and inverse folding. These models rely on a variety of architectures, including AlphaFold-class structure transformers, diffusion, flow matching, and graph neural networks. The fast modes we developed for structure prediction are statistically indistinguishable from the default settings across a pooled set of biomolecular interfaces. We call a predicted interface acceptable if its DockQ score is greater than 0.23. Enabling modeling of massive biomolecular systems In addition to making these protein structure prediction and design models faster, we also tasked Claude with reducing the memory usage involved in modeling large molecular machines.