Insilico Medicine to present groundbreaking AI retrosynthesis research at ICML 2026: Introducing ChemCensor to Bring Real-World Chemistry Into AI Benchmarking
Meeting Announcement
Updates every hour. Last Updated: 23-Jun-2026 04:16 ET (23-Jun-2026 08:16 GMT/UTC)
Insilico Medicine announced that its research paper, “When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs,” has been accepted for presentation at the International Conference on Machine Learning 2026. The study challenges conventional retrosynthesis benchmarking approaches that rely on single “ground-truth” answers and Top-K accuracy metrics, which may not reflect the multi-solution nature of real-world chemistry.
The paper introduces ChemCensor, a chemistry-aware evaluation metric designed to assess model performance based on reaction centers and functional groups, aligning more closely with expert human reasoning. Additional contributions include the CREED dataset, comprising 6.4 million validated reactions; benchmarking results from the C3LM model; and the URSA-expert-2026 dataset, an expert-annotated benchmark designed to reduce data leakage and improve evaluation rigor.
The research supports the development of more realistic and scalable training and evaluation frameworks for AI-driven retrosynthesis and drug discovery. Supporting materials will be made publicly available to promote transparency and reproducibility.
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