AI 学会像科学家一样看结构：从蛋白质到晶体，推理过程可解释
现在的 AI 做科学预测往往是黑箱：输入结构，输出结果，中间怎么想的没人知道。这篇让 AI 把结构拆成一个个「可寻址的证据单元」——比如一个原子、一个键、一个晶格——然后像科学家一样，基于这些单元和物理规则（立体化学、对称性、能量）一步步推理出结论。在蛋白质功能预测上，对低同源蛋白的准确率从 0.42 提到 0.55；在化学逆合成上，准确率从 0.63 提到 0.72，而且能给出断键和验证的痕迹。专家评估中，它的推理过程在 98% 的情况下优于或持平前沿大语言模型。它不是你明天能用上的工具，但它是第一个让 AI 在结构科学里「边想边给你看证据」的模型。
📄 原文摘要(英文)
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.