How Claude is uplifting biomolecular modeling

This post details how Anthropic used Claude to optimize over 30 deep learning models for biomolecular tasks (structure prediction, protein design, genomics, protein language models), achieving an average speedup of ~4x with minimal precision loss, and nearly 2x with identical outputs. It also describes the creation of a low-memory 'Big' mode enabling accurate modeling of systems larger than 10,000 tokens on a single GPU node. The post highlights the development of custom kernels (FlashPairformer) to accelerate triangle attention and multiplication operations in structure prediction models, outperforming existing standards. The optimized code for these models is open-sourced.