Nature computational science最新文献

Memory kernel minimization-based neural networks for discovering slow collective variables of biomolecular dynamics. 基于记忆核最小化的神经网络用于发现生物分子动力学的慢集体变量。

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Nature computational science Pub Date : 2025-06-10 DOI: 10.1038/s43588-025-00815-8

Bojun Liu, Siqin Cao, Jordan G Boysen, Mingyi Xue, Xuhui Huang

{"title":"Memory kernel minimization-based neural networks for discovering slow collective variables of biomolecular dynamics.","authors":"Bojun Liu, Siqin Cao, Jordan G Boysen, Mingyi Xue, Xuhui Huang","doi":"10.1038/s43588-025-00815-8","DOIUrl":"https://doi.org/10.1038/s43588-025-00815-8","url":null,"abstract":"Identifying collective variables (CVs) that accurately capture the slowest timescales of protein conformational changes is crucial to comprehend numerous biological processes. Here we introduce memory kernel minimization-based neural networks (MEMnets), a deep learning framework that accurately identifies the slow CVs of biomolecular dynamics. Unlike popular CV-identification methods, which typically assume Markovian dynamics, MEMnets is built on the integrative generalized master equation theory, which incorporates non-Markovian dynamics by encoding them in a memory kernel for continuous CVs. The key innovation of MEMnets is the identification of optimal CVs by minimizing the upper bound for the time-integrated memory kernels through parallel encoder networks. We demonstrate that MEMnets effectively identifies slow CVs involved in the folding of the FIP35 WW domain, revealing two parallel folding pathways. In addition, we illustrate MEMnets' robust numerical stability in identifying meaningful CVs in large biomolecular dynamic systems with limited sampling by applying it to the clamp opening of bacterial RNA polymerase, a much more complex conformational change.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging large language models for pandemic preparedness. 利用大型语言模型防备大流行病。

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Nature computational science Pub Date : 2025-06-06 DOI: 10.1038/s43588-025-00805-w

Narendra M Dixit

引用次数: 0

Advancing real-time infectious disease forecasting using large language models. 使用大型语言模型推进实时传染病预测。

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Nature computational science Pub Date : 2025-06-06 DOI: 10.1038/s43588-025-00798-6

Hongru Du, Yang Zhao, Jianan Zhao, Shaochong Xu, Xihong Lin, Yiran Chen, Lauren M Gardner, Hao 'Frank' Yang

{"title":"Advancing real-time infectious disease forecasting using large language models.","authors":"Hongru Du, Yang Zhao, Jianan Zhao, Shaochong Xu, Xihong Lin, Yiran Chen, Lauren M Gardner, Hao 'Frank' Yang","doi":"10.1038/s43588-025-00798-6","DOIUrl":"https://doi.org/10.1038/s43588-025-00798-6","url":null,"abstract":"Forecasting the short-term spread of an ongoing disease outbreak poses a challenge owing to the complexity of contributing factors, some of which can be characterized through interlinked, multi-modality variables, and the intersection of public policy and human behavior. Here we introduce PandemicLLM, a framework with multi-modal large language models (LLMs) that reformulates real-time forecasting of disease spread as a text-reasoning problem, with the ability to incorporate real-time, complex, non-numerical information. This approach, through an artificial intelligence-human cooperative prompt design and time-series representation learning, encodes multi-modal data for LLMs. The model is applied to the COVID-19 pandemic, and trained to utilize textual public health policies, genomic surveillance, spatial and epidemiological time-series data, and is tested across all 50 states of the United States for a duration of 19 months. PandemicLLM opens avenues for incorporating various pandemic-related data in heterogeneous formats and shows performance benefits over existing models.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A committor-based method to uniformly sample rare reactive events. 一种基于提交者的方法，用于统一采样罕见的反应事件。

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Nature computational science Pub Date : 2025-06-06 DOI: 10.1038/s43588-025-00825-6

引用次数: 0

Unified deep learning framework for many-body quantum chemistry via Green's functions. 基于格林函数的多体量子化学统一深度学习框架。

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Nature computational science Pub Date : 2025-06-04 DOI: 10.1038/s43588-025-00810-z

Christian Venturella, Jiachen Li, Christopher Hillenbrand, Ximena Leyva Peralta, Jessica Liu, Tianyu Zhu

{"title":"Unified deep learning framework for many-body quantum chemistry via Green's functions.","authors":"Christian Venturella, Jiachen Li, Christopher Hillenbrand, Ximena Leyva Peralta, Jessica Liu, Tianyu Zhu","doi":"10.1038/s43588-025-00810-z","DOIUrl":"https://doi.org/10.1038/s43588-025-00810-z","url":null,"abstract":"Quantum many-body methods provide a systematic route to computing electronic properties of molecules and materials, but high computational costs restrict their use in large-scale applications. Owing to the complexity in many-electron wavefunctions, machine learning models capable of capturing fundamental many-body physics remain limited. Here we present a deep learning framework targeting the many-body Green's function, which unifies predictions of electronic properties in ground and excited states, while offering physical insights into many-electron correlation effects. By learning the many-body perturbation theory or coupled-cluster self-energy from mean-field features, our graph neural network achieves competitive performance in predicting one- and two-particle excitations and quantities derivable from a one-particle density matrix. We demonstrate its high data efficiency and good transferability across chemical species, system sizes, molecular conformations and correlation strengths in bond breaking, through multiple molecular and nanomaterial benchmarks. This work opens up opportunities for utilizing machine learning to solve many-electron problems.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How molecular changes impact brain states and whole-brain activity: a multiscale approach. 分子变化如何影响大脑状态和全脑活动：多尺度方法。

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Nature computational science Pub Date : 2025-06-02 DOI: 10.1038/s43588-025-00813-w

引用次数: 0

Interpretable niche-based cell‒cell communication inference using multi-view graph neural networks. 利用多视图图神经网络可解释的基于小生境的细胞-细胞通信推理。

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Nature computational science Pub Date : 2025-05-27 DOI: 10.1038/s43588-025-00809-6

Juntian Qi, Zhengchao Luo, Chuan-Yun Li, Jinzhuo Wang, Wanqiu Ding

{"title":"Interpretable niche-based cell‒cell communication inference using multi-view graph neural networks.","authors":"Juntian Qi, Zhengchao Luo, Chuan-Yun Li, Jinzhuo Wang, Wanqiu Ding","doi":"10.1038/s43588-025-00809-6","DOIUrl":"https://doi.org/10.1038/s43588-025-00809-6","url":null,"abstract":"Cell‒cell communication (CCC) is a fundamental biological process for the harmonious functioning of biological systems. Increasing evidence indicates that cells of the same type or cluster may exhibit different interaction patterns under varying niches, yet most prevailing methods perform CCC inference at the cell type or cluster level while disregarding niche heterogeneity. Here we introduce the Spatial Transcriptomics-based cell‒cell Communication And Subtype Exploration (STCase) tool, which can describe CCC events at the single-cell/spot level based on spatial transcriptomics (ST). STCase includes an interpretable multi-view graph neural network via CCC-aware attention to identify niches for each cell type and uncover niche-specific CCC events. We show that STCase outperforms state-of-the-art approaches and accurately captures reported immune-related CCC events in human bronchial glands. We also identify three distinct niches of oral squamous cell carcinoma that may be obscured by agglomerative methods, and discover niche-specific CCC events that could influence tumor prognosis.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144163304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When disruption endures. 当破坏持续时。

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Nature computational science Pub Date : 2025-05-27 DOI: 10.1038/s43588-025-00812-x

Russell J Funk, Xiangting Wu

引用次数: 0

The critical role of persistent disruption in advancing science. 持续的破坏在推动科学发展中的关键作用。

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Nature computational science Pub Date : 2025-05-20 DOI: 10.1038/s43588-025-00808-7

Nan Deng, Xifeng Gu, Ying Fan, Shlomo Havlin, An Zeng

{"title":"The critical role of persistent disruption in advancing science.","authors":"Nan Deng, Xifeng Gu, Ying Fan, Shlomo Havlin, An Zeng","doi":"10.1038/s43588-025-00808-7","DOIUrl":"10.1038/s43588-025-00808-7","url":null,"abstract":"Disruptive innovation is an important feature of scientific research. However, increasing evidence in recent years shows that highly disruptive papers are not necessarily milestone works in science and may even receive very few citations. To understand the mechanisms leading to such phenomena, we develop a link disruption metric that quantifies the disruptiveness of each citation link. This metric allows us to investigate disruption at both the reference and citation levels, enabling the development of a two-dimensional framework to evaluate the persistence of disruption caused by a given paper. Surprisingly, we find that papers with high reference disruption can have high citation disruption, meaning that a paper that disrupts previous papers may itself be further disrupted by its later citing papers. We find that persistently disruptive papers (disruptive papers that are not disrupted by citing papers) are more likely to be recognized as award-winning papers and receive high numbers of citations. Finally, we find that papers of larger teams and papers in recent years, though found to have weaker disruption, are more likely to have stronger persistent disruption once they disrupt previous papers.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational challenges arising in algorithmic fairness and health equity with generative AI. 生成式人工智能在算法公平和健康公平方面产生的计算挑战。

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Nature computational science Pub Date : 2025-05-16 DOI: 10.1038/s43588-025-00806-9

Vinith M Suriyakumar, Anna Zink, Maia Hightower, Marzyeh Ghassemi, Brett Beaulieu-Jones

引用次数: 0