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Fostering discussions on topical issues 促进对热点问题的讨论。
IF 12
Nature computational science Pub Date : 2024-10-23 DOI: 10.1038/s43588-024-00719-z
{"title":"Fostering discussions on topical issues","authors":"","doi":"10.1038/s43588-024-00719-z","DOIUrl":"10.1038/s43588-024-00719-z","url":null,"abstract":"Nature Computational Science invites researchers to submit Correspondence pieces.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"723-723"},"PeriodicalIF":12.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00719-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514179","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
Author Correction: Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation 作者更正：用于高效 ab initio 电子结构计算的深度学习密度泛函理论哈密顿。
IF 12
Nature computational science Pub Date : 2024-10-23 DOI: 10.1038/s43588-024-00723-3
He Li, Zun Wang, Nianlong Zou, Meng Ye, Runzhang Xu, Xiaoxun Gong, Wenhui Duan, Yong Xu
{"title":"Author Correction: Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation","authors":"He Li, Zun Wang, Nianlong Zou, Meng Ye, Runzhang Xu, Xiaoxun Gong, Wenhui Duan, Yong Xu","doi":"10.1038/s43588-024-00723-3","DOIUrl":"10.1038/s43588-024-00723-3","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"876-876"},"PeriodicalIF":12.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00723-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514177","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
Taking a deep dive with active learning for drug discovery 利用主动学习深入研究药物发现。
IF 12
Nature computational science Pub Date : 2024-10-23 DOI: 10.1038/s43588-024-00704-6
Zachary Fralish, Daniel Reker
{"title":"Taking a deep dive with active learning for drug discovery","authors":"Zachary Fralish, Daniel Reker","doi":"10.1038/s43588-024-00704-6","DOIUrl":"10.1038/s43588-024-00704-6","url":null,"abstract":"Active machine learning is employed in academia and industry to support drug discovery. A recent study unravels the factors that influence a deep learning models’ ability to guide iterative discovery.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"727-728"},"PeriodicalIF":12.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514180","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
An interdisciplinary effort to integrate coding into science courses 将编码纳入科学课程的跨学科努力。
IF 12
Nature computational science Pub Date : 2024-10-22 DOI: 10.1038/s43588-024-00708-2
Christina L. Vizcarra, Ryan F. Trainor, Ashley Ringer McDonald, Chris T. Richardson, Davit Potoyan, Jessica A. Nash, Britt Lundgren, Tyler Luchko, Glen M. Hocky, Jonathan J. Foley IV, Timothy J. Atherton, Grace Y. Stokes
{"title":"An interdisciplinary effort to integrate coding into science courses","authors":"Christina L. Vizcarra, Ryan F. Trainor, Ashley Ringer McDonald, Chris T. Richardson, Davit Potoyan, Jessica A. Nash, Britt Lundgren, Tyler Luchko, Glen M. Hocky, Jonathan J. Foley IV, Timothy J. Atherton, Grace Y. Stokes","doi":"10.1038/s43588-024-00708-2","DOIUrl":"10.1038/s43588-024-00708-2","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 11","pages":"803-804"},"PeriodicalIF":12.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514176","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
The future of machine learning for small-molecule drug discovery will be driven by data 小分子药物发现机器学习的未来将由数据驱动。
IF 12
Nature computational science Pub Date : 2024-10-15 DOI: 10.1038/s43588-024-00699-0
Guy Durant, Fergus Boyles, Kristian Birchall, Charlotte M. Deane
{"title":"The future of machine learning for small-molecule drug discovery will be driven by data","authors":"Guy Durant, Fergus Boyles, Kristian Birchall, Charlotte M. Deane","doi":"10.1038/s43588-024-00699-0","DOIUrl":"10.1038/s43588-024-00699-0","url":null,"abstract":"Many studies have prophesied that the integration of machine learning techniques into small-molecule therapeutics development will help to deliver a true leap forward in drug discovery. However, increasingly advanced algorithms and novel architectures have not always yielded substantial improvements in results. In this Perspective, we propose that a greater focus on the data for training and benchmarking these models is more likely to drive future improvement, and explore avenues for future research and strategies to address these data challenges. The application of machine learning techniques to small-molecule drug discovery has not yet yielded a true leap forward in the field. This Perspective discusses how a renewed focus on data and validation could help unlock machine learning’s potential.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"735-743"},"PeriodicalIF":12.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482518","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
The decomposition of perturbation modeling 扰动建模的分解。
IF 12
Nature computational science Pub Date : 2024-10-14 DOI: 10.1038/s43588-024-00706-4
Stefan Peidli
{"title":"The decomposition of perturbation modeling","authors":"Stefan Peidli","doi":"10.1038/s43588-024-00706-4","DOIUrl":"10.1038/s43588-024-00706-4","url":null,"abstract":"A recent study proposes a strategy for the prediction of genetic perturbation outcomes by breaking it down into three subtasks: identifying differentially expressed genes, determining expression change directions, and estimating gene expression magnitudes.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"725-726"},"PeriodicalIF":12.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482517","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
Efficient simulations of electronic spectra 高效模拟电子光谱。
IF 12
Nature computational science Pub Date : 2024-10-14 DOI: 10.1038/s43588-024-00715-3
Kaitlin McCardle
{"title":"Efficient simulations of electronic spectra","authors":"Kaitlin McCardle","doi":"10.1038/s43588-024-00715-3","DOIUrl":"10.1038/s43588-024-00715-3","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"724-724"},"PeriodicalIF":12.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482516","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
Effectively detecting anomalous diffusion via deep learning 通过深度学习有效检测异常扩散。
IF 12
Nature computational science Pub Date : 2024-10-11 DOI: 10.1038/s43588-024-00705-5
Adrian Pacheco-Pozo, Diego Krapf
{"title":"Effectively detecting anomalous diffusion via deep learning","authors":"Adrian Pacheco-Pozo, Diego Krapf","doi":"10.1038/s43588-024-00705-5","DOIUrl":"10.1038/s43588-024-00705-5","url":null,"abstract":"A deep learning algorithm is presented to classify single-particle tracking trajectories into theoretical models of anomalous diffusion and detect if the trajectory is related to a model not originally found within the training dataset.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"731-732"},"PeriodicalIF":12.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407284","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
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。
IF 12
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。
IF 12
Nature computational science Pub Date : 2024-10-10 DOI: 10.1038/s43588-024-00707-3
Attila Cangi
{"title":"Bridging the gap in electronic structure calculations via machine learning","authors":"Attila Cangi","doi":"10.1038/s43588-024-00707-3","DOIUrl":"10.1038/s43588-024-00707-3","url":null,"abstract":"A highly efficient reconstruction method has been developed for the direct computation of Hamiltonian matrices in the atomic orbital basis from density functional theory calculations originally performed in the plane wave basis. This enables machine learning calculations of electronic structures on a large scale, which are otherwise not feasible with standard methods, and thus fills a methodological gap in terms of accessible length scales.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 10","pages":"729-730"},"PeriodicalIF":12.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402279","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
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