Structure-property relationships are foundational to biology, chemistry, and materials science, where function, reactivity, and physical responses emerge from spatial, chemical, and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through scientific principles and physical constraints, from stereochemistry and bonding to symmetry, energetics, and periodic order. However, applying artificial intelligence to this process presents a joint challenge of representation and reasoning: models must preserve domain-native structural information while showing how specific evidence supports predictions under these constraints.
Here we introduce SciReasoner, a multimodal scientific foundation model for native structural reasoning across proteins, small molecules, and inorganic crystals. SciReasoner discretizes coordinates, topologies, and periodic connectivities into a unified structure-aware vocabulary, treating structural tokens as addressable evidence units during reasoning.
In homology-controlled Gene Ontology prediction, SciReasoner improves Cellular Component annotation for low-homology and orphan-like proteins, increasing $F_{\max}$ from 0.42 to 0.55. In chemistry, it raises single-step retrosynthesis accuracy from 0.63 to 0.72 while generating fragment-level disconnection and precursor-verification traces. In materials science, its representations separate elemental and compound phases and resolve high- and low-band-gap regimes. Across 86 benchmarks, SciReasoner achieves state-of-the-art performance on 67 tasks. Double-blind expert evaluation rates its reasoning traces as preferred or at least comparable to those of a frontier large language model in 98% of cases. By making structure an inspectable substrate for reasoning under scientific constraints, SciReasoner connects accurate prediction with interpretable scientific inference.
Blogger's Review: SciReasoner represents a significant advancement in the integration of AI with structural reasoning, providing a robust framework for understanding complex relationships in various scientific domains. Its ability to enhance prediction accuracy while maintaining interpretability is crucial for the future of interdisciplinary research.