Nature computational science最新文献

英文中文

A scalable tool for fast and flexible variant identification in mass spectrometry 一个可扩展的工具，用于快速和灵活的质谱变异识别。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-12-09 DOI: 10.1038/s43588-025-00933-3

Bart Ghesquiere

Mass spectrometry data analysis has long been limited to known molecules and exact matches. In a recent manuscript, a scalable search algorithm is proposed for uncovering both known compounds and novel molecular variants, enabling insights into natural product biosynthesis.

质谱数据分析长期以来仅限于已知分子和精确匹配。在最近的一份手稿中，提出了一种可扩展的搜索算法，用于发现已知化合物和新的分子变异，从而深入了解天然产物的生物合成。

引用次数: 0

AUTOENCODIX: a generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond AUTOENCODIX：一个通用的和通用的框架来训练和评估自编码器的生物表征学习和超越。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-12-09 DOI: 10.1038/s43588-025-00916-4

Maximilian Josef Joas, Neringa Jurenaite, Dušan Praščević, Nico Scherf, Jan Ewald

In recent years, autoencoders, a family of deep learning-based methods for representation learning, are advancing data-driven research owing to their variability and nonlinear power for multimodal data integration. Despite their success, current implementations lack standardization, versatility, comparability and generalizability. Here we present AUTOENCODIX, an open-source framework, designed as a standardized and flexible pipeline for preprocessing, training and evaluation of autoencoder architectures. These architectures, such as ontology-based and cross-modal autoencoders, provide key advantages over traditional methods by offering explainability of embeddings or the ability to translate across data modalities. We apply the method to datasets from pan-cancer studies (The Cancer Genome Atlas) and single-cell sequencing as well as in combination with imaging. Our studies provide important user-centric insights and recommendations to navigate through architectures, hyperparameters and important tradeoffs in representation learning. These include the reconstruction capability of input data, the quality of embedding for downstream machine learning models and the reliability of ontology-based embeddings for explainability. An open-source framework called AUTOENCODIX is developed to enable reproducible comparison of vanilla, variational, stacked, ontology-based and cross-modal autoencoders.

近年来，自编码器作为一种基于深度学习的表示学习方法，由于其多变性和多模态数据集成的非线性能力，正在推进数据驱动研究。尽管它们取得了成功，但目前的实现缺乏标准化、通用性、可比性和通用性。在这里，我们提出了AUTOENCODIX，一个开源框架，被设计为一个标准化和灵活的管道，用于预处理，训练和评估自动编码器架构。这些架构，如基于本体和跨模态的自编码器，通过提供嵌入的可解释性或跨数据模态转换的能力，提供了优于传统方法的关键优势。我们将该方法应用于泛癌症研究（癌症基因组图谱）和单细胞测序以及结合成像的数据集。我们的研究提供了重要的以用户为中心的见解和建议，以导航架构，超参数和表示学习中的重要权衡。这些包括输入数据的重建能力、下游机器学习模型的嵌入质量以及基于本体的可解释性嵌入的可靠性。

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引用次数: 0

SciSciGPT: advancing human-AI collaboration in the science of science. sciigpt：推动人类与人工智能在科学领域的合作。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-12-09 DOI: 10.1038/s43588-025-00906-6

Erzhuo Shao, Yifang Wang, Yifan Qian, Zhenyu Pan, Han Liu, Dashun Wang

We introduce SciSciGPT, an open-source, prototype artificial intelligence (AI) collaborator that uses the domain of science of science as a testbed to explore the potential of large language model-powered research tools. SciSciGPT automates complex workflows, supports diverse analytical approaches, accelerates research prototyping and iteration and facilitates reproducibility. Through case studies, we demonstrate its ability to streamline a wide range of empirical and analytical research tasks while highlighting its broader potential to advance research. We further propose a large language model agent capability maturity model for human-AI collaboration, envisioning a roadmap to further improve and expand upon frameworks such as SciSciGPT. As AI capabilities continue to evolve, frameworks such as SciSciGPT may play increasingly pivotal roles in scientific research and discovery. At the same time, these new advances also raise critical challenges, from ensuring transparency and ethical use to balancing human and AI contributions. Addressing these issues may shape the future of scientific inquiry and inform how we train the next generation of scientists to thrive in an increasingly AI-integrated research ecosystem.

我们介绍了SciSciGPT，一个开源的，原型人工智能（AI）合作者，它使用科学的科学领域作为测试平台，探索大型语言模型驱动的研究工具的潜力。SciSciGPT自动化复杂的工作流程，支持多种分析方法，加速研究原型和迭代，并促进再现性。通过案例研究，我们展示了其简化广泛的实证和分析研究任务的能力，同时突出了其推进研究的更广泛潜力。我们进一步提出了一个用于人类-人工智能协作的大型语言模型代理能力成熟度模型，并展望了进一步改进和扩展诸如SciSciGPT等框架的路线图。随着人工智能能力的不断发展，SciSciGPT等框架可能在科学研究和发现中发挥越来越重要的作用。与此同时，这些新进展也带来了重大挑战，从确保透明度和道德使用到平衡人类和人工智能的贡献。解决这些问题可能会塑造科学探究的未来，并告知我们如何训练下一代科学家在日益集成人工智能的研究生态系统中茁壮成长。

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引用次数: 0

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs 基于深度学习的复杂纳米颗粒异质结构梯度优化。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-12-08 DOI: 10.1038/s43588-025-00917-3

Eric Sivonxay, Lucas Attia, Evan Walter Clark Spotte-Smith, Benjamin Sanchez-Lengeling, Xiaojing Xia, Daniel Barter, Emory M. Chan, Samuel M. Blau

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials. Graph neural networks built on physically motivated representations enable gradient-based optimization of complex upconverting nanoparticle heterostructures, revealing photophysical design rules and a roadmap for deep learning in nanoscience.

由于缺乏合适的数据表示和训练数据，深度学习（DL）在纳米材料设计中的应用受到阻碍。在这里，我们报告了克服这些限制的努力，并利用DL来优化核壳上转换纳米粒子（UCNPs）的非线性光学性质。UCNPs可以在近红外激发下发射可见光和紫外光，在生物传感、超分辨率显微镜和三维打印等领域有应用。我们报告了一个基于精确但昂贵的动力学蒙特卡罗模拟（N > 6000）的大规模UCNP发射光谱数据集，并使用这些数据来训练使用物理动机表示的UCNP纳米结构的异构图神经网络。在训练好的图神经网络上应用基于梯度的优化，我们识别出的结构在800 nm光照下的预测发射比我们训练集中的任何UCNP高6.5倍。我们的工作揭示了UCNP异质结构的设计原则，并提出了基于dl的纳米材料逆设计的路线图。

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引用次数: 0

Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings Scouter用大的语言模型嵌入来预测基因扰动的转录反应。

IF 18.3 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Nature computational science

Pub Date : 2025-12-05 DOI: 10.1038/s43588-025-00912-8

Ouyang Zhu, Jun Li

Gene perturbation experiments followed by transcriptomic profiling are vital for uncovering causal gene effects. However, their limited throughput leaves many perturbations of interest unexplored. Computational methods are therefore needed to predict genome-wide transcriptional responses to gene perturbations that were not experimentally assayed within a given dataset. Existing approaches often rely on Gene Ontology graphs to encode prior knowledge, but their predictive power and applicability are constrained by the graphs’ sparsity and incomplete gene coverage. Here we present Scouter, a computational method that uses gene embeddings generated by large language models and a lightweight compressor–generator neural network. Scouter accurately predicts transcriptional responses to both single- and two-gene perturbations, reducing errors from state-of-the-art Gene Ontology-term-based methods (GEARS and biolord) by half or more. Unlike recent approaches based on fine-tuning gene expression foundation models, Scouter offers substantially better accuracy and greater accessibility; it requires no pretraining and runs efficiently on standard hardware. A lightweight AI method called Scouter that predicts genome-wide transcriptional responses to single- and two-gene perturbations using large language model embeddings is presented and achieves substantially higher accuracy than leading approaches.

基因扰动实验随后转录组分析是至关重要的揭示因果基因效应。然而，它们有限的吞吐量使许多感兴趣的扰动未被探索。因此，需要计算方法来预测基因扰动对全基因组的转录反应，而这些基因扰动在给定数据集中没有实验分析。现有的方法通常依赖于基因本体图来编码先验知识，但其预测能力和适用性受到图的稀疏性和不完全基因覆盖的限制。在这里，我们提出了Scouter，一种使用大型语言模型和轻量级压缩生成器神经网络生成的基因嵌入的计算方法。Scouter准确地预测了单基因和双基因扰动的转录反应，将最先进的基于基因本体论术语的方法（GEARS和biolord）的错误减少了一半或更多。与最近基于微调基因表达基础模型的方法不同，Scouter提供了更好的准确性和更大的可访问性；它不需要预训练，在标准硬件上高效运行。

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引用次数: 0

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

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