Nature computational science最新文献_第3页

Efficient simulations of electronic spectra 高效模拟电子光谱。

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Nature computational science Pub Date : 2024-10-14 DOI: 10.1038/s43588-024-00715-3

Kaitlin McCardle

引用次数: 0

Effectively detecting anomalous diffusion via deep learning 通过深度学习有效检测异常扩散。

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Nature computational science Pub Date : 2024-10-11 DOI: 10.1038/s43588-024-00705-5

Adrian Pacheco-Pozo, Diego Krapf

引用次数: 0

Reliable deep learning in anomalous diffusion against out-of-distribution dynamics 在异常扩散中进行可靠的深度学习，对抗分布外动态。

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Nature computational science Pub Date : 2024-10-11 DOI: 10.1038/s43588-024-00703-7

Xiaochen Feng, Hao Sha, Yongbing Zhang, Yaoquan Su, Shuai Liu, Yuan Jiang, Shangguo Hou, Sanyang Han, Xiangyang Ji

{"title":"Reliable deep learning in anomalous diffusion against out-of-distribution dynamics","authors":"Xiaochen Feng, Hao Sha, Yongbing Zhang, Yaoquan Su, Shuai Liu, Yuan Jiang, Shangguo Hou, Sanyang Han, Xiangyang Ji","doi":"10.1038/s43588-024-00703-7","DOIUrl":"10.1038/s43588-024-00703-7","url":null,"abstract":"Anomalous diffusion plays a crucial rule in understanding molecular-level dynamics by offering valuable insights into molecular interactions, mobility states and the physical properties of systems across both biological and materials sciences. Deep-learning techniques have recently outperformed conventional statistical methods in anomalous diffusion recognition. However, deep-learning networks are typically trained by data with limited distribution, which inevitably fail to recognize unknown diffusion models and misinterpret dynamics when confronted with out-of-distribution (OOD) scenarios. In this work, we present a general framework for evaluating deep-learning-based OOD dynamics-detection methods. We further develop a baseline approach that achieves robust OOD dynamics detection as well as accurate recognition of in-distribution anomalous diffusion. We demonstrate that this method enables a reliable characterization of complex behaviors across a wide range of experimentally diverse systems, including nicotinic acetylcholine receptors in membranes, fluorescent beads in dextran solutions and silver nanoparticles undergoing active endocytosis. This work introduces a framework that enhances deep learning for anomalous diffusion, enabling reliable detection of out-of-distribution dynamics and characterization of complex behaviors across diverse systems.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"761-772"},"PeriodicalIF":12.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407285","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 in electronic structure calculations via machine learning 通过机器学习缩小电子结构计算的差距。

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Nature computational science Pub Date : 2024-10-10 DOI: 10.1038/s43588-024-00707-3

Attila Cangi

引用次数: 0

Publisher Correction: Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS 出版商更正：利用 MMIDAS 联合推断单细胞数据集中的离散细胞类型和连续类型特异性变异。

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Nature computational science Pub Date : 2024-10-03 DOI: 10.1038/s43588-024-00711-7

Yeganeh Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül

引用次数: 0

Generalizing deep learning electronic structure calculation to the plane-wave basis 将深度学习电子结构计算推广到平面波基础。

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Nature computational science Pub Date : 2024-10-03 DOI: 10.1038/s43588-024-00701-9

Xiaoxun Gong, Steven G. Louie, Wenhui Duan, Yong Xu

{"title":"Generalizing deep learning electronic structure calculation to the plane-wave basis","authors":"Xiaoxun Gong, Steven G. Louie, Wenhui Duan, Yong Xu","doi":"10.1038/s43588-024-00701-9","DOIUrl":"10.1038/s43588-024-00701-9","url":null,"abstract":"Deep neural networks capable of representing the density functional theory (DFT) Hamiltonian as a function of material structure hold great promise for revolutionizing future electronic structure calculations. However, a notable limitation of previous neural networks is their compatibility solely with the atomic-orbital (AO) basis, excluding the widely used plane-wave (PW) basis. Here we overcome this critical limitation by proposing an accurate and efficient real-space reconstruction method for directly computing AO Hamiltonian matrices from PW DFT results. The reconstruction method is orders of magnitude faster than traditional projection-based methods to convert PW results to the AO basis, and the reconstructed Hamiltonian matrices can faithfully reproduce the PW electronic structure, thus bridging the longstanding gap between the AO basis deep learning electronic structure approach and PW DFT. Advantages of the PW methods, such as high accuracy, high flexibility and wide applicability, thus can be all integrated into deep learning electronic structure methods without sacrificing these methods’ inherent benefits. This allows for the construction of large-scale and high-fidelity training datasets with the help of PW DFT results towards the development of precise and broadly applicable deep learning electronic structure models. Deep learning electronic structure calculations are generalized from the atomic-orbital basis to the plane-wave basis, resulting in higher accuracy, improved transferability and the capability to utilize existing electronic structure big data.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"752-760"},"PeriodicalIF":12.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11499277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Publisher Correction: Increasing the presence of BIPOC researchers in computational science 出版商更正：增加计算科学领域的黑人和印地安人研究人员。

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Nature computational science Pub Date : 2024-10-01 DOI: 10.1038/s43588-024-00710-8

Christine Yifeng Chen, Alan Christoffels, Roger Dube, Kamuela Enos, Juan E. Gilbert, Sanmi Koyejo, Jason Leigh, Carlo Liquido, Amy McKee, Kari Noe, Tai-Quan Peng, Karaitiana Taiuru

引用次数: 0

Traversing chemical space with active deep learning for low-data drug discovery 利用主动深度学习穿越化学空间，实现低数据药物发现。

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Nature computational science Pub Date : 2024-09-27 DOI: 10.1038/s43588-024-00697-2

Derek van Tilborg, Francesca Grisoni

{"title":"Traversing chemical space with active deep learning for low-data drug discovery","authors":"Derek van Tilborg, Francesca Grisoni","doi":"10.1038/s43588-024-00697-2","DOIUrl":"10.1038/s43588-024-00697-2","url":null,"abstract":"Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, in terms of either size or molecular diversity. Active deep learning has high potential for low-data drug discovery, as it allows iterative model improvement during the screening process. However, there are several ‘known unknowns’ that limit the wider adoption of active deep learning in drug discovery: (1) what the best computational strategies are for chemical space exploration, (2) how active learning holds up to traditional, non-iterative, approaches and (3) how it should be used in the low-data scenarios typical of drug discovery. To provide answers, this study simulates a low-data drug discovery scenario, and systematically analyzes six active learning strategies combined with two deep learning architectures, on three large-scale molecular libraries. We identify the most important determinants of success in low-data regimes and show that active learning can achieve up to a sixfold improvement in hit discovery when compared with traditional screening methods. Active deep learning is a promising approach to learn from low-data scenarios in drug discovery. This study illuminates key success factors of active learning and shows that it can boost hit discovery by up to sixfold over traditional methods.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"786-796"},"PeriodicalIF":12.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333950","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

Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond 基于子任务分解的学习和基准，用于预测遗传扰动结果及其他。

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Nature computational science Pub Date : 2024-09-27 DOI: 10.1038/s43588-024-00698-1

Yicheng Gao, Zhiting Wei, Kejing Dong, Ke Chen, Jingya Yang, Guohui Chuai, Qi Liu

{"title":"Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond","authors":"Yicheng Gao, Zhiting Wei, Kejing Dong, Ke Chen, Jingya Yang, Guohui Chuai, Qi Liu","doi":"10.1038/s43588-024-00698-1","DOIUrl":"10.1038/s43588-024-00698-1","url":null,"abstract":"Deciphering cellular responses to genetic perturbations is fundamental for a wide array of biomedical applications. However, there are three main challenges: predicting single-genetic-perturbation outcomes, predicting multiple-genetic-perturbation outcomes and predicting genetic outcomes across cell lines. Here we introduce Subtask Decomposition Modeling for Genetic Perturbation Prediction (STAMP), a flexible artificial intelligence strategy for genetic perturbation outcome prediction and downstream applications. STAMP formulates genetic perturbation prediction as a subtask decomposition problem by resolving three progressive subtasks in a problem decomposition manner, that is, identifying postperturbation differentially expressed genes, determining the expression change directions of differentially expressed genes and finally estimating the magnitudes of gene expression changes. STAMP exhibits a substantial improvement over the existing approaches on three subtasks and beyond, including the ability to identify key regulatory genes and pathways on small samples and to reveal precise genetic interactions of diverse types. By employing the subtask decomposition strategy, STAMP outperforms existing models in single, multiple and cross-cell-line scenarios for genetic perturbation prediction, showing potential to uncover gene regulations and interactions.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"773-785"},"PeriodicalIF":12.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333949","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

Increasing the presence of BIPOC researchers in computational science 增加 BIPOC 研究人员在计算科学领域的存在

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Nature computational science Pub Date : 2024-09-24 DOI: 10.1038/s43588-024-00693-6

Christine Yifeng Chen, Alan Christoffels, Roger Dube, Kamuela Enos, Juan E. Gilbert, Sanmi Koyeji, Jason Leigh, Carlo Liquido, Amy McKee, Kari Noe, Tai-Quan Peng, Karaitiana Taiuru

引用次数: 0