基於深度原生結構推理的精準、跨學科且透明的結構-性質理解

摘要

结构-性质关系是生物学、化学和材料科学的基础，功能、反应性和物理响应均源于空间、化学和周期性的组织。对这些关系进行机理上的解释，需要借助科学原理和物理约束来解读结构证据，涵盖立体化学、成键、对称性、能量学和周期性有序等层面。然而，将人工智能应用于这一过程带来了表征与推理的双重挑战：模型必须保留领域原生的结构信息，同时展示在特定约束下具体证据如何支持预测。在此，我们提出SciReasoner，一个面向蛋白质、小分子和无机晶体的原生结构推理多模态科学基础模型。SciReasoner将坐标、拓扑结构和周期性连接离散化为统一的结构感知词汇表，在推理过程中将结构标记视为可寻址的证据单元。在同源性控制的基因本体预测中，SciReasoner改善了对低同源性及类似孤儿蛋白的细胞组分注释，将F_max从0.42提升至0.55。在化学领域，它将单步逆合成准确率从0.63提升至0.72，并生成了片段级断键与前体验证轨迹。在材料科学中，其表征能够区分元素相与化合物相，并分辨高带隙与低带隙区域。在86个基准测试中，SciReasoner在67个任务上达到了最先进水平。双盲专家评估显示，在98%的情况下，其推理轨迹被评价为优于或至少与前沿大语言模型相当。通过将结构作为在科学约束下可检验的推理基础，SciReasoner将准确预测与可解释的科学推理连接起来。

English

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.