Nature Computational Science
Nature Computational Science(英文缩写 NAT COMPUT SCI),ISSN 2662-8457,eISSN 2662-8457 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间:2025-09-21 至 2026-09-21,按本站收录文献的发表日期统计。
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期刊简介
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Nature Computational Science 最新收录文献
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1. Large language models as human proxies.
PMID:作者:日期:2026-09-18The idea of computationally approximating human behavior is being reshaped by the emergence of large language models (LLMs). Although LLMs do not explain the mechanisms of human behavior, they can potentially stand in for humans-giving rise to new applied and scientific uses. Here we analyze researc…
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2. Revisiting molecular descriptors with TDiMS for interpretable intramolecular interactions based on substructure pairs.
PMID:作者:日期:2026-09-17Molecular descriptors play a crucial role in representing the structural features of molecules for machine learning-based physical property prediction. However, current descriptors either consider only local aspects of molecular structures or fail to effectively learn nonlocal structural features in…
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5. MutexaGPT: an intuition-to-design translator for physics-based enzyme engineering.
PMID:作者:日期:2026-09-10Physical intuition about how enzyme structure and dynamics shape function has guided successful engineering efforts, yet a systematic approach is still lacking for translating these qualitative and abstract 'thoughts' into quantitative, actionable principles for enzyme design. Here we introduce Mute…
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6. Segment anything in pathology images with natural language.
PMID:作者:日期:2026-09-10Segmenting tissues and cells in pathology images enables quantitative analysis but usually requires task-specific models or repeated spatial prompts. Here we show PathSegmentor, a foundation model that uses natural language descriptions to segment structures across anatomical regions and spatial sca…
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7. Predicting brain morphogenesis via physics-transfer learning.
PMID:作者:日期:2026-09-09Brain morphology emerges from the interplay of genetic programming and mechanical forces, yet its fractal-like folding patterns make quantitative analysis and prediction difficult, especially when labeled data are scarce. Here we introduce a theory-grounded physics-transfer learning framework that e…