蛋白质折叠架构发现

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难度：高级

适用场景：Computational biologists exploring architecture, loss, or curriculum changes against an automatically scorable benchmark. Researchers who have a scientifically motivated hypothesis and want to compress the path from idea to working experimental fork. ML engineers running long-lived autoresearch loops that require persistent experiment tracking and iterative debugging.

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and the public NanoFold benchmark. The benchmark provides a small, curated fixed-data and automatically scorable substrate for structural-biology experimentation. Keep the first implementation small enough to test with targeted unit tests and microbenchmarks before launching expensive training runs. Run the search with Goal Mode Supply a falsifiable, high-level scientific hypothesis instead of asking the model to invent an entire research agenda from scratch. Use GPT-5.5 Pro in ChatGPT to...