Nature computational science最新文献_第6页

Programmable responsive metamaterials for mechanical computing and robotics 用于机械计算和机器人技术的可编程响应超材料。

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Nature computational science Pub Date : 2024-08-27 DOI: 10.1038/s43588-024-00673-w

Qiguang He, Samuele Ferracin, Jordan R. Raney

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Computational design of art-inspired metamaterials 艺术启发超材料的计算设计。

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Nature computational science Pub Date : 2024-08-27 DOI: 10.1038/s43588-024-00671-y

Gary P. T. Choi

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Synergy between photonic metamaterials and AI 光子超材料与人工智能的协同作用。

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Nature computational science Pub Date : 2024-08-27 DOI: 10.1038/s43588-024-00675-8

Jie Pan

引用次数: 0

AI-recognized mitochondrial phenotype enables identification of drug targets 人工智能识别的线粒体表型有助于确定药物靶点。

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Nature computational science Pub Date : 2024-08-22 DOI: 10.1038/s43588-024-00682-9

引用次数: 0

Deep learning large-scale drug discovery and repurposing 深度学习大规模药物发现和再利用。

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Nature computational science Pub Date : 2024-08-21 DOI: 10.1038/s43588-024-00679-4

Min Yu, Weiming Li, Yunru Yu, Yu Zhao, Lizhi Xiao, Volker M. Lauschke, Yiyu Cheng, Xingcai Zhang, Yi Wang

{"title":"Deep learning large-scale drug discovery and repurposing","authors":"Min Yu, Weiming Li, Yunru Yu, Yu Zhao, Lizhi Xiao, Volker M. Lauschke, Yiyu Cheng, Xingcai Zhang, Yi Wang","doi":"10.1038/s43588-024-00679-4","DOIUrl":"10.1038/s43588-024-00679-4","url":null,"abstract":"Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing. A deep learning-based model, MitoReID, is presented for profiling changes in mitochondrial phenotypes, allowing for the identification of various drugs’ mechanism of action.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 8","pages":"600-614"},"PeriodicalIF":12.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019787","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

Bridging the gap between artificial intelligence and natural intelligence 缩小人工智能与自然智能之间的差距。

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Nature computational science Pub Date : 2024-08-16 DOI: 10.1038/s43588-024-00677-6

Rui-Jie Zhu, Skye Gunasekaran, Jason Eshraghian

引用次数: 0

Accelerating predictions of electronic transport and superconductivity 加速电子传输和超导预测。

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Nature computational science Pub Date : 2024-08-16 DOI: 10.1038/s43588-024-00678-5

Ting Cao

引用次数: 0

Network model with internal complexity bridges artificial intelligence and neuroscience 具有内部复杂性的网络模型是人工智能和神经科学的桥梁。

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Nature computational science Pub Date : 2024-08-16 DOI: 10.1038/s43588-024-00674-9

Linxuan He, Yunhui Xu, Weihua He, Yihan Lin, Yang Tian, Yujie Wu, Wenhui Wang, Ziyang Zhang, Junwei Han, Yonghong Tian, Bo Xu, Guoqi Li

{"title":"Network model with internal complexity bridges artificial intelligence and neuroscience","authors":"Linxuan He, Yunhui Xu, Weihua He, Yihan Lin, Yang Tian, Yujie Wu, Wenhui Wang, Ziyang Zhang, Junwei Han, Yonghong Tian, Bo Xu, Guoqi Li","doi":"10.1038/s43588-024-00674-9","DOIUrl":"10.1038/s43588-024-00674-9","url":null,"abstract":"Artificial intelligence (AI) researchers currently believe that the main approach to building more general model problems is the big AI model, where existing neural networks are becoming deeper, larger and wider. We term this the big model with external complexity approach. In this work we argue that there is another approach called small model with internal complexity, which can be used to find a suitable path of incorporating rich properties into neurons to construct larger and more efficient AI models. We uncover that one has to increase the scale of the network externally to stimulate the same dynamical properties. To illustrate this, we build a Hodgkin–Huxley (HH) network with rich internal complexity, where each neuron is an HH model, and prove that the dynamical properties and performance of the HH network can be equivalent to a bigger leaky integrate-and-fire (LIF) network, where each neuron is a LIF neuron with simple internal complexity. This study shows that by enhancing internal complexity of neurons in a Hodgkin–Huxley network, similar performance to larger, simpler networks can be achieved, suggesting an alternative path for powerful AI systems by focusing on neuron complexity.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 8","pages":"584-599"},"PeriodicalIF":12.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141997048","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

Enhancing structured light with optimized metasurfaces 利用优化的元表面增强结构光。

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Nature computational science Pub Date : 2024-08-16 DOI: 10.1038/s43588-024-00684-7

Jie Pan

引用次数: 0

Accelerating the calculation of electron–phonon coupling strength with machine learning 利用机器学习加速电子-声子耦合强度的计算。

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Nature computational science Pub Date : 2024-08-08 DOI: 10.1038/s43588-024-00668-7

Yang Zhong, Shixu Liu, Binhua Zhang, Zhiguo Tao, Yuting Sun, Weibin Chu, Xin-Gao Gong, Ji-Hui Yang, Hongjun Xiang

{"title":"Accelerating the calculation of electron–phonon coupling strength with machine learning","authors":"Yang Zhong, Shixu Liu, Binhua Zhang, Zhiguo Tao, Yuting Sun, Weibin Chu, Xin-Gao Gong, Ji-Hui Yang, Hongjun Xiang","doi":"10.1038/s43588-024-00668-7","DOIUrl":"10.1038/s43588-024-00668-7","url":null,"abstract":"The calculation of electron–phonon couplings (EPCs) is essential for understanding various fundamental physical properties, including electrical transport, optical and superconducting behaviors in materials. However, obtaining EPCs through fully first-principles methods is notably challenging, particularly for large systems or when employing advanced functionals. Here we introduce a machine learning framework to accelerate EPC calculations by utilizing atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network. We demonstrate that our method not only yields EPC values in close agreement with first-principles results but also enhances calculation efficiency by several orders of magnitude. Application to GaAs using the Heyd–Scuseria–Ernzerhof functional reveals the necessity of advanced functionals for accurate carrier mobility predictions, while for the large Kagome crystal CsV3Sb5, our framework reproduces the experimentally observed double domes in pressure-induced superconducting phase diagrams. This machine learning framework offers a powerful and efficient tool for the investigation of diverse EPC-related phenomena in complex materials. A machine learning framework is proposed to accurately predict electron–phonon coupling (EPC) strengths while reducing computational costs compared with first-principles methods. This approach facilitates EPC calculations with advanced functionals, allowing the accurate determination of real-world material properties such as carrier mobility and superconductivity.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 8","pages":"615-625"},"PeriodicalIF":12.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908581","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