Nature computational science最新文献

Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts.

IF 12

Nature computational science Pub Date : 2025-03-28 DOI: 10.1038/s43588-025-00783-z

Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang, Fujie Tang, Zhifeng Gao, Linfeng Zhang, Weinan E, Zhong-Qun Tian, Jun Cheng

引用次数: 0

Towards transparency and knowledge exchange in AI-assisted data analysis code generation.

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Nature computational science Pub Date : 2025-03-27 DOI: 10.1038/s43588-025-00781-1

Robert Haase

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Integrating statistical physics and machine learning for combinatorial optimization.

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Nature computational science Pub Date : 2025-03-26 DOI: 10.1038/s43588-025-00794-w

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An organelle-agnostic image analysis tool

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Nature computational science Pub Date : 2025-03-24 DOI: 10.1038/s43588-025-00785-x

Michelle Badri

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Free-energy machine for combinatorial optimization.

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Nature computational science Pub Date : 2025-03-24 DOI: 10.1038/s43588-025-00782-0

Zi-Song Shen, Feng Pan, Yao Wang, Yi-Ding Men, Wen-Biao Xu, Man-Hong Yung, Pan Zhang

{"title":"Free-energy machine for combinatorial optimization.","authors":"Zi-Song Shen, Feng Pan, Yao Wang, Yi-Ding Men, Wen-Biao Xu, Man-Hong Yung, Pan Zhang","doi":"10.1038/s43588-025-00782-0","DOIUrl":"10.1038/s43588-025-00782-0","url":null,"abstract":"Finding optimal solutions to combinatorial optimization problems (COPs) is pivotal in both scientific and industrial domains. Considerable efforts have been invested on developing accelerated methods utilizing sophisticated models and advanced computational hardware. However, the challenge remains to achieve both high efficiency and broad generality in problem-solving. Here we propose a general method, free-energy machine (FEM), based on the ideas of free-energy minimization in statistical physics, combined with automatic differentiation and gradient-based optimization in machine learning. FEM flexibly addresses various COPs within a unified framework and efficiently leverages parallel computational devices such as graphics processing units. We benchmark FEM on diverse COPs including maximum cut, balanced minimum cut and maximum k-satisfiability, scaled to millions of variables, across synthetic and real-world instances. The findings indicate that FEM remarkably outperforms state-of-the-art algorithms tailored for individual COP in both efficiency and efficacy, demonstrating the potential of combining statistical physics and machine learning for broad applications.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702183","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

Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort

IF 12

Nature computational science Pub Date : 2025-03-20 DOI: 10.1038/s43588-025-00774-0

Nicole Osayande, Justin Marotta, Shambhavi Aggarwal, Jakub Kopal, Avram Holmes, Sarah W. Yip, Danilo Bzdok

{"title":"Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort","authors":"Nicole Osayande, Justin Marotta, Shambhavi Aggarwal, Jakub Kopal, Avram Holmes, Sarah W. Yip, Danilo Bzdok","doi":"10.1038/s43588-025-00774-0","DOIUrl":"10.1038/s43588-025-00774-0","url":null,"abstract":"Despite the mounting demand for generative population models, their limited generalizability to underrepresented demographic groups hinders widespread adoption in real-world applications. Here we propose a diversity-aware population modeling framework that can guide targeted strategies in public health and education, by estimating subgroup-level effects and stratifying predictions to capture sociodemographic variability. We leverage Bayesian multilevel regression and post-stratification to systematically quantify inter-individual differences in the relationship between socioeconomic status and cognitive development. Post-stratification enhanced the interpretability of model predictions across underrepresented groups by incorporating US Census data to gain additional insights into smaller subgroups in the Adolescent Brain Cognitive Development Study. This ensured that predictions were not skewed by overly heterogeneous or homogeneous representations. Our analyses underscore the importance of combining Bayesian multilevel modeling with post-stratification to validate reliability and provide a more holistic explanation of sociodemographic disparities in our diversity-aware population modeling framework. The study proposes a diversity-aware population modeling framework that can guide targeted strategies in public health, using Bayesian multilevel regression and post-stratification to quantify sociodemographic disparities in cognitive development.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 3","pages":"221-233"},"PeriodicalIF":12.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143671983","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

Diversity-aware population modeling

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Nature computational science Pub Date : 2025-03-20 DOI: 10.1038/s43588-025-00787-9

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A dynamic block activation framework for continuum models.

IF 12

Nature computational science Pub Date : 2025-03-17 DOI: 10.1038/s43588-025-00780-2

Ruoyao Zhang, Yang Xia

{"title":"A dynamic block activation framework for continuum models.","authors":"Ruoyao Zhang, Yang Xia","doi":"10.1038/s43588-025-00780-2","DOIUrl":"https://doi.org/10.1038/s43588-025-00780-2","url":null,"abstract":"Efficient utilization of massively parallel computing resources is crucial for advancing scientific understanding through complex simulations. However, existing adaptive methods often face challenges in implementation complexity and scalability on modern parallel hardware. Here we present dynamic block activation (DBA), an acceleration framework that can be applied to a broad range of continuum simulations by strategically allocating resources on the basis of the dynamic features of the physical model. By exploiting the hierarchical structure of parallel hardware and dynamically activating and deactivating computation blocks, DBA optimizes performance while maintaining accuracy. We demonstrate DBA's effectiveness through solving representative models spanning multiple scientific fields, including materials science, biophysics and fluid dynamics, achieving 216-816 central processing unit core-equivalent speedups on a single graphics processing unit (GPU), up to fivefold acceleration compared with highly optimized GPU code and nearly perfect scalability up to 32 GPUs. By addressing common challenges, such as divergent memory access, and reducing programming burden, DBA offers a promising approach to fully leverage massively parallel systems across multiple scientific computing domains.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652544","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

Boosting crystal structure prediction via symmetry

IF 12

Nature computational science Pub Date : 2025-03-13 DOI: 10.1038/s43588-025-00779-9

Yanchao Wang

引用次数: 0

Rapid traversal of vast chemical space using machine learning-guided docking screens.

IF 12

Nature computational science Pub Date : 2025-03-13 DOI: 10.1038/s43588-025-00777-x

Andreas Luttens, Israel Cabeza de Vaca, Leonard Sparring, José Brea, Antón Leandro Martínez, Nour Aldin Kahlous, Dmytro S Radchenko, Yurii S Moroz, María Isabel Loza, Ulf Norinder, Jens Carlsson

{"title":"Rapid traversal of vast chemical space using machine learning-guided docking screens.","authors":"Andreas Luttens, Israel Cabeza de Vaca, Leonard Sparring, José Brea, Antón Leandro Martínez, Nour Aldin Kahlous, Dmytro S Radchenko, Yurii S Moroz, María Isabel Loza, Ulf Norinder, Jens Carlsson","doi":"10.1038/s43588-025-00777-x","DOIUrl":"https://doi.org/10.1038/s43588-025-00777-x","url":null,"abstract":"The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for the fastest structure-based docking methods. Here we explore a strategy that combines machine learning and molecular docking to enable rapid virtual screening of databases containing billions of compounds. In our workflow, a classification algorithm is trained to identify top-scoring compounds based on molecular docking of 1 million compounds to the target protein. The conformal prediction framework is then used to make selections from the multi-billion-scale library, reducing the number of compounds to be scored by docking. The CatBoost classifier showed an optimal balance between speed and accuracy and was used to adapt the workflow for screens of ultralarge libraries. Application to a library of 3.5 billion compounds demonstrated that our protocol can reduce the computational cost of structure-based virtual screening by more than 1,000-fold. Experimental testing of predictions identified ligands of G protein-coupled receptors and demonstrated that our approach enables discovery of compounds with multi-target activity tailored for therapeutic effect.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627187","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