MolStruct: A Benchmark for Multi-Scale Molecular Structure Understanding, Reasoning, and Prediction

Foundation models have shown growing capabilities in molecular understanding and structure editing, yet their ability to reason about how molecular interventions shape dynamic behavior remains largely unexplored. In this work, we introduce MolStruct, a benchmark for molecular structural reasoning built around an intervention-relaxation pipeline. MolStruct covers molecular structure at three levels of granularity: atoms and bonds, functional groups, and whole molecules and evaluates four capabilities: structural perception, inverse change reasoning, stability reasoning, and structural prediction, in both text-based and multimodal settings. Experiments across multiple open-source and proprietary models show that current models perform reasonably well on perception, but struggle substantially with inverse reasoning, stability reasoning and predicting post-intervention relaxed outcomes. The results indicate that models are relatively stronger at recognizing molecular structures than at judging their stability or predicting how they relax. We further find that increasing model scale or reasoning depth provides only limited improvement, highlighting molecular stability reasoning as an important unresolved challenge for current foundation models.