A modest but open theorem in theoretical biology proved by AI (twitter.com)

A recent preprint has revealed a modest yet significant theorem in theoretical biology, affirmatively proving the "Designability of RNA Targets with Up to Two Length-2 Helices" using AI assistance. The study, led by a chemist and biologist, leveraged sophisticated AI tools like GPT/Codex and Claude Code through multiple adversarial review cycles to construct a mathematically rigorous proof. The research demonstrates that for a specific class of RNA targets, there exists a unique sequence capable of achieving the desired secondary structure, thereby ensuring optimal pairing interactions and minimum energy states. This theorem not only addresses a previously unproven subclass within RNA folding but also highlights a novel synergy between human expertise and AI in scientific inquiry. The implication of this work for the AI/ML community is substantial, showcasing the potential of AI to assist in solving complex mathematical problems in fields traditionally reliant on experimental validation, such as biology and chemistry. By formalizing and verifying the proof in Lean, the researchers underscored the versatile capabilities of AI in generating and validating mathematical arguments. This approach may pave the way for more interdisciplinary collaborations, encouraging scientists from various backgrounds to explore advanced computational methods for rigorous theoretical exploration. The project also poses intriguing questions about the role of AI in scientific validation, encouraging further discourse on the nature of argumentation and proof in mathematics and beyond.