SciReasoner: A Multimodal Scientific Foundation Model for Native Structural Reasoning Across Proteins, Small Molecules and Inorganic Crystals
Score
81首次实现跨蛋白质、小分子和晶体结构的统一本征推理，显著超越现有方法并验证推理质量，提供了全新范式。
方法描述清晰，实验覆盖86个基准、多学科验证，并包含专家评估，数据详实。
在药物设计、材料发现等场景有直接应用潜力，但需进一步验证在真实世界任务中的部署效果。
2025年7月发表的预印本，紧跟可解释科学AI和基础模型前沿，时效性极强。
来自arXiv预印本，虽未经同行评审，但基于开放标准且URL可访问，通常具有较高可信度。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
The paper introduces an innovative structural reasoning paradigm, achieving significant performance gains across multiple disciplines and validating reasoning quality through expert evaluation, demonstrating strong technical completeness and interdisciplinary impact.
AI commentary
「This work really stands out to me—it makes a genuine attempt to address the 'black box' pain point of AI in science by turning structural tokens into reasonable evidence, combining accuracy gains with interpretability. For researchers in computational chemistry, structural biology, or materials genomics, SciReasoner's interdisciplinary capability and expert-validation results are an important reference. Notably, the authors have open-sourced the model and reasoning traces, making the ecosystem development worth watching.」
Quick overview
This paper introduces SciReasoner, a multimodal scientific foundation model that performs native structural reasoning across proteins, small molecules, and inorganic crystals. By discretizing coordinates, topologies, and periodic connectivities into a unified structure-aware vocabulary, it treats structural tokens as addressable evidence units during reasoning. Across interdisciplinary benchmarks, SciReasoner achieves state-of-the-art performance on 67 out of 86 tasks—for example, improving Gene Ontology Cellular Component annotation Fmax from 0.42 to 0.55 for low-homology proteins, and raising single-step retrosynthesis accuracy from 0.63 to 0.72. Double-blind expert evaluations show its reasoning traces are preferred or comparable to a frontier large language model in 98% of cases.
- SciReasoner converts spatial coordinates, topologies, and periodic connectivities into a unified structure-aware discrete vocabulary, enabling the model to reason with structure as addressable evidence.
- Across 86 benchmarks in biology, chemistry, and materials science, SciReasoner achieves state-of-the-art on 67 tasks, covering Gene Ontology prediction, retrosynthesis analysis, and band-gap classification.
- Double-blind expert evaluations show SciReasoner's reasoning traces are preferred or comparable to a frontier LLM in 98% of cases.
Full text
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