Anthropic used Claude to optimize 36 biomolecular modeling packages, achieving up to 4.1× speedup in four weeks

AI 参与科研的稳妥姿势：让 Claude 去优化科学软件本身

Two Anthropic researchers with biomodeling expertise but no GPU kernel background spent under four weeks with Claude refactoring 36 open-source biomolecular packages. They built FlashPairformer, a custom GPU kernel that fuses scattered triangle-attention ops into high-throughput streaming, then applied per-model caching and CUDA graph replay. Benchmarked on H100 against a hand-tuned expert baseline, the bitwise-identical exact mode averages 1.6× speedup; the fast mode, which allows noise within the model's own stochastic range, averages 4.1×; the memory-saving big mode averages 3.4×. Exact and fast modes can push memory up to 3×. DockQ acceptable rates stayed at 54–55% across modes, with no systematic accuracy loss. The report draws clear lines: big mode ran a 10,761-token complex at TM-score 0.92–0.997, but on 31k–70k-residue viral capsids the outputs collapsed into dense balls (TM-score 0.08–0.14). The authors attribute this to the model's 768-token training-crop limit, not the optimizations. In protein design, a single Claude instance driving optimized models on one H200 for 24 hours hit a median ipSAE of 0.785, up from 0.749 in the earlier multi-agent campaign, but none of the designs have been wet-lab tested. Code is open-sourced under Apache-2.0 with no ongoing maintenance.

Why it matters: Anthropic researchers used Claude to refactor 30+ biomolecular model codebases in under four weeks, shipping FlashPairformer kernels and reproducible optimizations. Concrete technical details, open-source code, measured results — not a fluff piece. Points off: this is a yage.a...