Pub Date : 2026-02-06DOI: 10.1038/s42256-026-01193-0
Roxana Radu, Luc Rocher
{"title":"Attributing and situating knowledge cannot be left to language models","authors":"Roxana Radu, Luc Rocher","doi":"10.1038/s42256-026-01193-0","DOIUrl":"https://doi.org/10.1038/s42256-026-01193-0","url":null,"abstract":"","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"3 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1038/s42256-025-01171-y
Urs J. Muehlematter, Kerstin Noelle Vokinger
{"title":"Authorization of prognostic AI medical devices","authors":"Urs J. Muehlematter, Kerstin Noelle Vokinger","doi":"10.1038/s42256-025-01171-y","DOIUrl":"https://doi.org/10.1038/s42256-025-01171-y","url":null,"abstract":"","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"53 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1038/s42256-026-01179-y
Gene Tangtartharakul, Katherine R. Storrs
{"title":"Visual language models show widespread visual deficits on neuropsychological tests","authors":"Gene Tangtartharakul, Katherine R. Storrs","doi":"10.1038/s42256-026-01179-y","DOIUrl":"https://doi.org/10.1038/s42256-026-01179-y","url":null,"abstract":"","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"240 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1038/s42256-026-01178-z
Raffaele Ciriello
{"title":"On the troubling rise of generative AI suspicion in academic publishing","authors":"Raffaele Ciriello","doi":"10.1038/s42256-026-01178-z","DOIUrl":"https://doi.org/10.1038/s42256-026-01178-z","url":null,"abstract":"","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"282 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liquid electrolytes are critical components of next-generation energy storage systems, enabling fast ion transport, minimizing interfacial resistance and ensuring electrochemical stability for long-term battery performance. However, measuring electrolyte properties and designing formulations remain experimentally and computationally expensive. Here we present a unified framework for designing liquid electrolyte formulation, integrating a forward predictive model with an inverse generative approach. Leveraging both computational and experimental data collected from the literature and extensive molecular simulations, we train a predictive model capable of accurately estimating electrolyte properties from ionic conductivity to solvation structure. Our physics-informed architecture preserves permutation invariance and incorporates empirical dependencies on temperature and salt concentration, making it broadly applicable to property prediction tasks across molecular mixtures. Furthermore, we introduce a generative machine learning framework for molecular mixture design with permutation invariance, demonstrated on electrolyte systems. This framework supports multi-condition-constrained generation, addressing the inherently multi-objective nature of materials design. As a proof of concept, we experimentally identified three liquid electrolytes exhibiting both high ionic conductivity and anion-rich solvation structures, one of which shows promising cycling stability. This unified framework advances data-driven electrolyte design and can be readily extended to other complex chemical systems beyond electrolytes.
{"title":"A unified predictive and generative solution for liquid electrolyte formulation","authors":"Zhenze Yang, Yifan Wu, Xu Han, Ziqing Zhang, Haoen Lai, Zhenliang Mu, Tianze Zheng, Siyuan Liu, Zhichen Pu, Zhi Wang, Zhiao Yu, Sheng Gong, Wen Yan","doi":"10.1038/s42256-025-01173-w","DOIUrl":"https://doi.org/10.1038/s42256-025-01173-w","url":null,"abstract":"Liquid electrolytes are critical components of next-generation energy storage systems, enabling fast ion transport, minimizing interfacial resistance and ensuring electrochemical stability for long-term battery performance. However, measuring electrolyte properties and designing formulations remain experimentally and computationally expensive. Here we present a unified framework for designing liquid electrolyte formulation, integrating a forward predictive model with an inverse generative approach. Leveraging both computational and experimental data collected from the literature and extensive molecular simulations, we train a predictive model capable of accurately estimating electrolyte properties from ionic conductivity to solvation structure. Our physics-informed architecture preserves permutation invariance and incorporates empirical dependencies on temperature and salt concentration, making it broadly applicable to property prediction tasks across molecular mixtures. Furthermore, we introduce a generative machine learning framework for molecular mixture design with permutation invariance, demonstrated on electrolyte systems. This framework supports multi-condition-constrained generation, addressing the inherently multi-objective nature of materials design. As a proof of concept, we experimentally identified three liquid electrolytes exhibiting both high ionic conductivity and anion-rich solvation structures, one of which shows promising cycling stability. This unified framework advances data-driven electrolyte design and can be readily extended to other complex chemical systems beyond electrolytes.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"73 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1038/s42256-026-01183-2
Agentic artificial intelligence (AI) frameworks are in vogue. However, implementing such systems in scientific research workflows requires clear motivations and explanations, given the risk of wasting computational as well as human resources.
{"title":"Multi-agent AI systems need transparency","authors":"","doi":"10.1038/s42256-026-01183-2","DOIUrl":"10.1038/s42256-026-01183-2","url":null,"abstract":"Agentic artificial intelligence (AI) frameworks are in vogue. However, implementing such systems in scientific research workflows requires clear motivations and explanations, given the risk of wasting computational as well as human resources.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 1","pages":"1-1"},"PeriodicalIF":23.9,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s42256-026-01183-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1038/s42256-025-01165-w
Yue Guo, Hao Zhang, Haitao Hu, Jialu Wu, Ji Cao, Chang-Yu Hsieh, Bo Yang
Systematic mapping of chemical perturbation responses is revolutionizing polypharmacological drug discovery, yet remains constrained by experimental scalability. Here we introduce XPert, a biologically informed dual-branch transformer model designed to model gene-specific perturbation effects and dose–time dynamics. The dual-branch architecture separately encodes pre-perturbation and post-perturbation cellular states, allowing the model to disentangle intrinsic transcriptional patterns from regulatory shifts triggered by perturbations. By leveraging context-aware gene network modelling, XPert overcomes the over-denoising issues inherent in dominant variational-autoencoder-based approaches, achieving 36.7% higher Pearson’s correlation coefficient and 78.2% lower mean square error in cold-cell generalization under single-dose–single-time scenarios. Through extension to multidose–multitime prediction, XPert precisely resolves pharmacodynamic trajectories and uncovers key molecular events underlying the drug effects. To address real-world data scarcity, we apply knowledge transfer from large-scale preclinical screens to clinical contexts, achieving up to 15.04% improvement in patient-specific response predictions. Furthermore, XPert provides mechanistic interpretability, as evidenced by the identification of clinically validated resistance biomarkers. A dual-branch framework that disentangles cell states from drug-induced regulatory shifts to predict transcriptional responses is presented. It captures nonlinear dose–time dynamics and excels in generalizing to unseen cellular contexts.
{"title":"Modelling drug-induced cellular perturbation responses with a biologically informed dual-branch transformer","authors":"Yue Guo, Hao Zhang, Haitao Hu, Jialu Wu, Ji Cao, Chang-Yu Hsieh, Bo Yang","doi":"10.1038/s42256-025-01165-w","DOIUrl":"10.1038/s42256-025-01165-w","url":null,"abstract":"Systematic mapping of chemical perturbation responses is revolutionizing polypharmacological drug discovery, yet remains constrained by experimental scalability. Here we introduce XPert, a biologically informed dual-branch transformer model designed to model gene-specific perturbation effects and dose–time dynamics. The dual-branch architecture separately encodes pre-perturbation and post-perturbation cellular states, allowing the model to disentangle intrinsic transcriptional patterns from regulatory shifts triggered by perturbations. By leveraging context-aware gene network modelling, XPert overcomes the over-denoising issues inherent in dominant variational-autoencoder-based approaches, achieving 36.7% higher Pearson’s correlation coefficient and 78.2% lower mean square error in cold-cell generalization under single-dose–single-time scenarios. Through extension to multidose–multitime prediction, XPert precisely resolves pharmacodynamic trajectories and uncovers key molecular events underlying the drug effects. To address real-world data scarcity, we apply knowledge transfer from large-scale preclinical screens to clinical contexts, achieving up to 15.04% improvement in patient-specific response predictions. Furthermore, XPert provides mechanistic interpretability, as evidenced by the identification of clinically validated resistance biomarkers. A dual-branch framework that disentangles cell states from drug-induced regulatory shifts to predict transcriptional responses is presented. It captures nonlinear dose–time dynamics and excels in generalizing to unseen cellular contexts.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 1","pages":"96-112"},"PeriodicalIF":23.9,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s42256-025-01165-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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