MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?
Large language models (LLMs) are increasingly used in molecular discovery and structure elucidation, where decisions depend on physical observations. Existing evaluations primarily focus on end-to-end molecular reconstruction, where correct predictions do not necessarily indicate reliance on the supplied properties. We introduce MolP2SBench, a benchmark for molecular property-to-structure (P2S) reasoning in LLMs. Each task presents a masked molecular scaffold with given properties and asks the model to select the missing structure from plausible candidates. We shuffle or replace properties to test reliance on correct property-structure pairing and responsiveness to counterfactual property...
| Authors | Yuhang Xie, Zhiyi Shi, Zeyi Zhang et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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