aceExperiences developing structural biology tools with AI coding assistance
Large language models (LLMs) offer significant potential to accelerate scientific software development, yet their application to structural biology algorithms requires rigorous evaluation. This challenge is examined through the development of xcif, a new native parser for the crystallographic information file (CIF) format, designed to replace a stable but slow parser (ucif) in the Computational Crystallography Toolbox (cctbx). Using an expert-led, artificial intelligence (AI)-assisted workflow, implementation ti…
With expert-led AI assistance, a replacement CIF parser was built in about one week of active human effort instead of an estimated several months, and it runs much faster than the legacy parser.
AI-generated code was plausible but flawed, so the work depended entirely on domain experts to specify constraints, catch errors and design adversarial tests.
The efficiency gain relied entirely on domain expertise to constrain the AI, catch flawed code and design adversarial tests, so the result may not hold without such oversight.
Evidence
- Peer-reviewedActa Crystallographica Section D Structural Biology2026-11-01
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Truvace Impact Record TRV-2026-1884, v1: “Experiences developing structural biology tools with AI coding assistance.” Truvace, 2026-10-19. /record/TRV-2026-1884 (accessed at citation time). sha256 232bcc6c3d31fce8…
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