Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
For scientists in biology, chemistry, and materials science, this model provides accurate and interpretable structure-property predictions, especially for low-homology proteins and retrosynthesis planning.
SciReasoner, a multimodal scientific foundation model, uses a unified structure-aware vocabulary to reason across proteins, small molecules, and inorganic crystals, achieving state-of-the-art performance on 67 of 86 benchmarks. It improves Gene Ontology prediction F_max from 0.42 to 0.55, retrosynthesis accuracy from 0.63 to 0.72, and its reasoning traces are preferred or comparable to a frontier LLM in 98% of cases.
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response 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.