Nature computational science最新文献

英文中文

Chemistry-informed deep learning model for predicting stereoselectivity and absolute configuration in asymmetric hydrogenation. 预测不对称氢化过程立体选择性和绝对构型的化学信息深度学习模型。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-12-05 DOI: 10.1038/s43588-025-00920-8

Li Cheng, Pan-Lin Shao, Jiahui Lv, Hongjun Xiao, Yanping Sun, Jingkai Yang, Ziyi Xu, Mingkun Lv, Guanghui Wang, Shaokang Zhao, Jiaxin Li, Ziqi Jin, Xuan Tan, Guichuan Xing, Bo Zhang

The asymmetric hydrogenation of olefins is one of the most important asymmetric transformations in molecular synthesis. While other machine learning models have successfully predicted stereoselectivity for reactions with a single prochiral site, existing models face limitations including narrow substrate-catalyst applicability, an inability to simultaneously predict stereoselectivity and absolute configurations in asymmetric hydrogenation of olefins with two prochiral sites, and a reliance on predefined descriptors. Here, to overcome these challenges, we introduce Chemistry-Informed Asymmetric Hydrogenation Network (ChemAHNet), a deep learning model based on the reaction mechanism of olefin asymmetric hydrogenation. By leveraging three structure-aware modules, ChemAHNet accurately predicts the absolute configuration of major enantiomers across diverse catalysts and substrates. It also defines the $ΔΔ G^{‡}$ of asymmetric hydrogenation via catalyst-olefin interactions, enabling concurrent prediction of stereoselectivity and absolute configuration. Notably, ChemAHNet extends to other asymmetric catalytic reactions. By operating solely on simplified molecular-input line-entry system inputs, it captures atomic-level spatial and electronic interactions, offering a robust tool for target-directed molecular engineering.

烯烃的不对称加氢是分子合成中最重要的不对称转化之一。虽然其他机器学习模型已经成功地预测了具有单个前手性位点的反应的立体选择性，但现有模型面临着局限性，包括底物-催化剂适用性窄，无法同时预测具有两个前手性位点的烯烃不对称加氢反应的立体选择性和绝对构型，以及依赖于预定义的描述符。为了克服这些挑战，我们引入了化学信息不对称氢化网络（ChemAHNet），这是一个基于烯烃不对称氢化反应机理的深度学习模型。通过利用三个结构感知模块，ChemAHNet可以准确预测不同催化剂和底物中主要对映体的绝对构型。它还定义了通过催化剂-烯烃相互作用的不对称氢化的ΔΔ G‡，从而可以同时预测立体选择性和绝对构型。值得注意的是，ChemAHNet扩展到其他不对称催化反应。通过仅操作简化的分子输入线输入系统输入，它可以捕获原子水平的空间和电子相互作用，为靶向分子工程提供了一个强大的工具。

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引用次数: 0

Revealing neurocognitive and behavioral patterns through unsupervised manifold learning of dynamic brain data 通过动态大脑数据的无监督多元学习揭示神经认知和行为模式。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-12-04 DOI: 10.1038/s43588-025-00911-9

Zixia Zhou, Junyan Liu, Wei Emma Wu, Ruogu Fang, Sheng Liu, Qingyue Wei, Rui Yan, Yi Guo, Qian Tao, Yuanyuan Wang, Md Tauhidul Islam, Lei Xing

Dynamic brain data are becoming increasingly accessible, providing a gateway to understanding the inner workings of the brain in living participants. However, the size and complexity of the data pose a challenge in extracting meaningful information across various data sources. Here we introduce a generalizable unsupervised deep manifold learning for exploration of neurocognitive and behavioral patterns. Unlike existing methods that extract patterns directly from the input data, the proposed brain-dynamic convolutional-network-based embedding (BCNE) captures brain-state trajectories by analyzing temporospatial correlations within the data and applying manifold learning. The results demonstrate that BCNE effectively delineates scene transitions, underscores the involvement of different brain regions in memory and narrative processing, distinguishes dynamic learning processes and identifies differences between active and passive behaviors. BCNE provides an effective tool for exploring general neuroscience inquiries or individual-specific patterns. BCNE, an unsupervised deep-learning method, reveals clear trajectories of brain activity and effectively distinguishes cognitive events, learning stages and active versus passive movement, outperforming traditional data visualization methods.

动态大脑数据正变得越来越容易获取，为了解活体参与者的大脑内部运作提供了一个途径。然而，数据的大小和复杂性给从各种数据源中提取有意义的信息带来了挑战。在这里，我们介绍了一种可推广的无监督深度流形学习，用于探索神经认知和行为模式。与直接从输入数据中提取模式的现有方法不同，本文提出的基于脑动态卷积网络的嵌入（BCNE）通过分析数据中的时空相关性和应用流形学习来捕获大脑状态轨迹。结果表明，BCNE能够有效地描述场景转换，强调不同脑区参与记忆和叙事加工，区分动态学习过程，识别主动和被动行为之间的差异。BCNE提供了一个有效的工具，用于探索一般神经科学查询或个人特定模式。

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引用次数: 0

Identifying variants of molecules through database search of mass spectra 通过质谱数据库搜索识别分子的变体。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-12-01 DOI: 10.1038/s43588-025-00923-5

Mustafa Guler, Benjamin Krummenacher, Thomas Hall, Meghana Tandon, Joshua Abrams, Sanjana Ravi, Peng Chen, Matthew Lauber, Bahar Behsaz, Hosein Mohimani

Mass spectrometry is a widely used method for the identification of molecules in complex samples. Current tools for database search of experimental spectra against libraries of molecules are not scalable. Moreover, these tools are often limited to known molecules and only perform an exact search. Here, to address this, we introduce Variable Interpretation of Spectrum–Molecule Couples, or VInSMoC, a mass spectral database search algorithm for the identification of variants of molecules. VInSMoC removes some false identifications by estimating the statistical significance of matches between spectra and molecular structures. Benchmarking VInSMoC in a search of 483 million spectra from GNPS against 87 million molecules from PubChem and COCONUT revealed 43,000 known molecules and 85,000 variants that were previously unreported. VInSMoC further facilitates identifying putative microbial biosynthesis pathways of promothiocin B and depsidomycin in Streptomyces bellus and Streptomyces sp. F-2747, respectively. The authors present a scalable mass spectral search tool that identifies both known molecules and structural variants by estimating match significance. The method revealed biosynthetic pathways in Streptomyces, expanding the scope of metabolite discovery.

质谱法是一种广泛应用于复杂样品中分子鉴定的方法。目前针对分子文库进行实验光谱数据库搜索的工具是不可扩展的。此外，这些工具通常仅限于已知的分子，只能进行精确的搜索。在这里，为了解决这个问题，我们引入了光谱-分子偶对的变量解释（VInSMoC），这是一种用于识别分子变体的质谱数据库搜索算法。VInSMoC通过估计光谱与分子结构匹配的统计显著性来消除一些错误的识别。通过对GNPS中的4.83亿个光谱与PubChem和COCONUT中的8700万个分子进行比对，VInSMoC发现了43,000个已知分子和85,000个以前未报道的变体。VInSMoC进一步帮助鉴定了bellus链霉菌和Streptomyces sp. F-2747中促硫霉素B和深霉素的微生物合成途径。

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引用次数: 0

Efficiently decoding quantum errors with machine learning 用机器学习有效解码量子错误。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-11-25 DOI: 10.1038/s43588-025-00907-5

Xiu-Hao Deng, Yuan Xu

Quantum computers are inching closer to practical deployment, but shielding fragile quantum information from errors is still very challenging. Now, a machine-learning-based decoder offers a strategy for rectifying errors in logic quantum circuits, hastening the advent of reliable and fault-tolerant quantum systems.

量子计算机离实际部署越来越近，但保护脆弱的量子信息不受错误的影响仍然非常具有挑战性。现在，一种基于机器学习的解码器提供了一种纠正逻辑量子电路错误的策略，加速了可靠和容错量子系统的出现。

引用次数: 0

Publisher Correction: On the compatibility of generative AI and generative linguistics 发布者更正：关于生成人工智能与生成语言学的兼容性。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-11-24 DOI: 10.1038/s43588-025-00937-z

Eva Portelance, Masoud Jasbi

引用次数: 0

Viability of using LLMs as models of human language processing. 使用llm作为人类语言处理模型的可行性。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-11-21 DOI: 10.1038/s43588-025-00913-7

Alex Murphy

引用次数: 0

Discovering physical laws with parallel symbolic enumeration 用并行符号枚举法发现物理定律。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-11-21 DOI: 10.1038/s43588-025-00904-8

Kai Ruan, Yilong Xu, Ze-Feng Gao, Yang Liu, Yike Guo, Ji-Rong Wen, Hao Sun

Symbolic regression has a crucial role in modern scientific research owing to its capability of discovering concise and interpretable mathematical expressions from data. A key challenge lies in the search for parsimonious and generalizable mathematical formulas, in an infinite search space, while intending to fit the training data. Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade when handling problems of complexity, which essentially hinders the pace of applying symbolic regression for scientific exploration across interdisciplinary domains. Here, to this end, we introduce parallel symbolic enumeration (PSE) to efficiently distill generic mathematical expressions from limited data. Experiments show that PSE achieves higher accuracy and faster computation compared with the state-of-the-art baseline algorithms across over 200 synthetic and experimental problem sets (for example, improving the recovery accuracy by up to 99% and reducing runtime by an order of magnitude). PSE represents an advance in accurate and efficient data-driven discovery of symbolic, interpretable models (for example, underlying physical laws), and improves the scalability of symbolic learning. In this work, the authors introduce parallel symbolic enumeration (PSE), a model that discovers physical laws from data with improved accuracy and speed. By evaluating millions of expressions in parallel and reusing computations, PSE outperforms the state-of-the-art methods.

符号回归能够从数据中发现简洁、可解释的数学表达式，在现代科学研究中发挥着至关重要的作用。一个关键的挑战在于在无限搜索空间中寻找简洁和可推广的数学公式，同时打算拟合训练数据。近十年来，符号回归算法在处理复杂问题时面临着精度和效率的瓶颈，这从根本上阻碍了符号回归在跨学科领域科学探索中的应用。为此，我们引入并行符号枚举（PSE）来从有限的数据中有效地提取通用的数学表达式。实验表明，与最先进的基线算法相比，PSE在超过200个合成和实验问题集上实现了更高的精度和更快的计算速度（例如，将恢复精度提高了99%，并将运行时间缩短了一个数量级）。PSE代表了对符号的、可解释的模型（例如，潜在的物理定律）的准确和有效的数据驱动发现的进步，并且提高了符号学习的可伸缩性。

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引用次数: 0

How to respond to reviewers 如何回应审稿人

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-11-21 DOI: 10.1038/s43588-025-00931-5

We provide recommendations on how to write an effective point-by-point response document.

我们就如何撰写有效的逐点回应文件提供建议。

引用次数: 0

Efficient methods for facilitating topological photonics and acoustics computation 促进拓扑光子学和声学计算的有效方法。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-11-21 DOI: 10.1038/s43588-025-00921-7

Zhaohui Dong, Luqi Yuan

A recent study proposes efficient numerical algorithms to reduce the required computational resources for solving the edge states in large-scale photonic or acoustic structures.

最近的一项研究提出了有效的数值算法来减少求解大规模光子或声学结构中边缘态所需的计算资源。

引用次数: 0

How LLMs generate judgments 法学硕士是如何做出判断的

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-11-20 DOI: 10.1038/s43588-025-00925-3

Fernando Chirigati

引用次数: 0

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