AI 学会像科学家一样看结构：从原子到功能
AI 通常把分子、蛋白质、晶体当黑箱学规律，但科学家看结构靠的是化学键、对称性、空间排布这些硬约束。这篇让 AI 也走这条路：把原子坐标、连接方式、周期排列拆成一个个可寻址的「结构词」，像读句子一样读结构，然后基于这些词做推理。在蛋白质功能预测上，对低同源蛋白（像孤儿蛋白）的标注准确率从 0.42 提到 0.55；在化学逆合成上，单步准确率从 0.63 到 0.72，还能给出断键位置和验证路径。专家盲评中，它的推理过程在 98% 案例里不输 GPT-4。它不是你明天能用上的工具，但它是第一个让 AI 的「结构直觉」可追溯、可审查的模型——科学家可以指着某个原子问「你为什么这么想」。
📄 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.