Nature Machine Intelligence最新文献_第2页

Machine learning prediction of enzyme optimum pH 酶最适pH的机器学习预测

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-29 DOI: 10.1038/s42256-025-01026-6

Japheth E. Gado, Matthew Knotts, Ada Y. Shaw, Debora Marks, Nicholas P. Gauthier, Chris Sander, Gregg T. Beckham

{"title":"Machine learning prediction of enzyme optimum pH","authors":"Japheth E. Gado, Matthew Knotts, Ada Y. Shaw, Debora Marks, Nicholas P. Gauthier, Chris Sander, Gregg T. Beckham","doi":"10.1038/s42256-025-01026-6","DOIUrl":"https://doi.org/10.1038/s42256-025-01026-6","url":null,"abstract":"The relationship between pH and enzyme catalytic activity, especially the optimal pH (pHopt) at which enzymes function, is critical for biotechnological applications. Hence, computational methods to predict pHopt will enhance enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at specific pH levels, and by elucidating sequence–function relationships. Here we proposed and evaluated various machine learning methods for predicting pHopt, conducting extensive hyperparameter optimization and training over 11,000 model instances. Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pHopt, including proximity of residues to the catalytic centre and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pHopt prediction and will potentially speed up the development of enzyme technologies.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"9 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884858","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}

引用次数: 0

Robot planning with LLMs 机器人规划与法学硕士

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-23 DOI: 10.1038/s42256-025-01036-4

引用次数: 0

Optimal transport for generating transition states in chemical reactions 化学反应中产生过渡态的最佳输运

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-23 DOI: 10.1038/s42256-025-01010-0

Chenru Duan, Guan-Horng Liu, Yuanqi Du, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik

{"title":"Optimal transport for generating transition states in chemical reactions","authors":"Chenru Duan, Guan-Horng Liu, Yuanqi Du, Tianrong Chen, Qiyuan Zhao, Haojun Jia, Carla P. Gomes, Evangelos A. Theodorou, Heather J. Kulik","doi":"10.1038/s42256-025-01010-0","DOIUrl":"https://doi.org/10.1038/s42256-025-01010-0","url":null,"abstract":"Transition states (TSs) are transient structures that are key to understanding reaction mechanisms and designing catalysts but challenging to capture in experiments. Many optimization algorithms have been developed to search for TSs computationally. Yet, the cost of these algorithms driven by quantum chemistry methods (usually density functional theory) is still high, posing challenges for their applications in building large reaction networks for reaction exploration. Here we developed React-OT, an optimal transport approach for generating unique TS structures from reactants and products. React-OT generates highly accurate TS structures with a median structural root mean square deviation of 0.053 Å and median barrier height error of 1.06 kcal mol−1 requiring only 0.4 s per reaction. The root mean square deviation and barrier height error are further improved by roughly 25% through pretraining React-OT on a large reaction dataset obtained with a lower level of theory, GFN2-xTB. We envision that the remarkable accuracy and rapid inference of React-OT will be highly useful when integrated with the current high-throughput TS search workflow. This integration will facilitate the exploration of chemical reactions with unknown mechanisms.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"1 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143867037","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}

引用次数: 0

Personalized uncertainty quantification in artificial intelligence 人工智能中的个性化不确定性量化

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-23 DOI: 10.1038/s42256-025-01024-8

Tapabrata Chakraborti, Christopher R. S. Banerji, Ariane Marandon, Vicky Hellon, Robin Mitra, Brieuc Lehmann, Leandra Bräuninger, Sarah McGough, Cagatay Turkay, Alejandro F. Frangi, Ginestra Bianconi, Weizi Li, Owen Rackham, Deepak Parashar, Chris Harbron, Ben MacArthur

引用次数: 0

Sparse and transferable three-dimensional dynamic vascular reconstruction for instantaneous diagnosis 用于瞬时诊断的稀疏和可转移的三维动态血管重建

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-21 DOI: 10.1038/s42256-025-01025-7

Yinheng Zhu, Yong Wang, Chunxia Di, Hanghang Liu, Fangzhou Liao, Shaohua Ma

{"title":"Sparse and transferable three-dimensional dynamic vascular reconstruction for instantaneous diagnosis","authors":"Yinheng Zhu, Yong Wang, Chunxia Di, Hanghang Liu, Fangzhou Liao, Shaohua Ma","doi":"10.1038/s42256-025-01025-7","DOIUrl":"https://doi.org/10.1038/s42256-025-01025-7","url":null,"abstract":"Three-dimensional (3D) structural information of cardiac vessels is crucial for the diagnosis and treatment of cardiovascular disease. In clinical practice, interventionalists have to empirically infer 3D cardiovascular topology from multi-view X-ray angiography images, which is time-consuming and requires extensive experience. Owing to the dynamic nature of heartbeats and sparse-view observations in clinical practice, accurate and efficient reconstruction of 3D cardiovascular structures from X-ray angiography images remains challenging. Here we introduce AutoCAR, a fully automated transfer learning-based algorithm for dynamic 3D cardiovascular reconstruction. AutoCAR comprises three main components: pose domain adaptation, sparse backwards projection and vascular graph optimization. By merging the X-ray angiography imaging parameter statistics of over 1,000 clinical cases into synthetic data generation, and exploiting the intrinsic spatial sparsity of cardiac vessels for computational design, AutoCAR outperforms state-of-the-art methods in both qualitative and quantitative evaluations, enabling dynamic cardiovascular reconstruction in real-world clinical settings. We envision that AutoCAR will facilitate current diagnostic and intervention procedures and pave the way for real-time visual guidance and autonomous catheter navigation in cardiac intervention.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"108 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853418","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}

引用次数: 0

Transforming machines capable of continuous 3D shape morphing and locking 能够连续三维变形和锁定的变形机器

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-18 DOI: 10.1038/s42256-025-01028-4

Shiwei Xu, Xiaonan Hu, Ruoxi Yang, Chuanqi Zang, Lei Li, Yue Xiao, Wenbo Liu, Bocheng Tian, Wenbo Pang, Renheng Bo, Qing Liu, Youzhou Yang, Yuchen Lai, Jun Wu, Huichan Zhao, Li Wen, Yihui Zhang

{"title":"Transforming machines capable of continuous 3D shape morphing and locking","authors":"Shiwei Xu, Xiaonan Hu, Ruoxi Yang, Chuanqi Zang, Lei Li, Yue Xiao, Wenbo Liu, Bocheng Tian, Wenbo Pang, Renheng Bo, Qing Liu, Youzhou Yang, Yuchen Lai, Jun Wu, Huichan Zhao, Li Wen, Yihui Zhang","doi":"10.1038/s42256-025-01028-4","DOIUrl":"https://doi.org/10.1038/s42256-025-01028-4","url":null,"abstract":"Inspired by natural species that leverage morphological changes to realize multiple locomotion modes, diverse multimodal robots have been reported. While developments of small-scale actuators with continuous shape morphing and locking capabilities controlled by the same energy source are crucial for miniaturization of untethered multimodal robots, it remains elusive. We introduce a synergistic design concept of small-scale continuously morphable actuators (CMAs) that harness precisely programmable actuation deformation of liquid crystal elastomer to achieve continuous shape morphing and high stiffness variation of shape memory polymer to lock geometric configuration, both through electrothermal control. Lego-inspired design strategy allows customized construction of complexly shaped CMAs (for example, ‘transformer’, ‘aircraft’ and ‘turtle’) through rational assembly of elementary actuator units with different ranges of accessible geometric configurations. The powerful shape morphing and locking capabilities, as well as the relatively high load-bearing capacity of the CMAs, allow for developments of versatile exoskeletons that can integrate a diversity of functional components. Demonstrations of unique small-scale transforming machines, such as morphable displays with a rich diversity of three-dimensional geometries, a wheeled microrobot capable of transformation among ‘sports car’, ‘winged car’ and ‘van’, and a lightweight untethered terrestrial–aerial microrobot, suggest a broad spectrum of applications.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"24 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847099","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}

引用次数: 0

AI safety for everyone 人人享有人工智能安全

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-17 DOI: 10.1038/s42256-025-01020-y

Bálint Gyevnár, Atoosa Kasirzadeh

{"title":"AI safety for everyone","authors":"Bálint Gyevnár, Atoosa Kasirzadeh","doi":"10.1038/s42256-025-01020-y","DOIUrl":"https://doi.org/10.1038/s42256-025-01020-y","url":null,"abstract":"Recent discussions and research in artificial intelligence (AI) safety have increasingly emphasized the deep connection between AI safety and existential risk from advanced AI systems, suggesting that work on AI safety necessarily entails serious consideration of potential existential threats. However, this framing has three potential drawbacks: it may exclude researchers and practitioners who are committed to AI safety but approach the field from different angles; it could lead the public to mistakenly view AI safety as focused solely on existential scenarios rather than addressing a wide spectrum of safety challenges; and it risks creating resistance to safety measures among those who disagree with predictions of existential AI risks. Here, through a systematic literature review of primarily peer-reviewed research, we find a vast array of concrete safety work that addresses immediate and practical concerns with current AI systems. This includes crucial areas such as adversarial robustness and interpretability, highlighting how AI safety research naturally extends existing technological and systems safety concerns and practices. Our findings suggest the need for an epistemically inclusive and pluralistic conception of AI safety that can accommodate the full range of safety considerations, motivations and perspectives that currently shape the field.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"1 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841844","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}

引用次数: 0

Human-centred design and fabrication of a wearable multimodal visual assistance system 以人为本设计和制造可穿戴多模态视觉辅助系统

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-14 DOI: 10.1038/s42256-025-01018-6

Jian Tang, Yi Zhu, Gai Jiang, Lin Xiao, Wei Ren, Yu Zhou, Qinying Gu, Biao Yan, Jiayi Zhang, Hengchang Bi, Xing Wu, Zhiyong Fan, Leilei Gu

{"title":"Human-centred design and fabrication of a wearable multimodal visual assistance system","authors":"Jian Tang, Yi Zhu, Gai Jiang, Lin Xiao, Wei Ren, Yu Zhou, Qinying Gu, Biao Yan, Jiayi Zhang, Hengchang Bi, Xing Wu, Zhiyong Fan, Leilei Gu","doi":"10.1038/s42256-025-01018-6","DOIUrl":"https://doi.org/10.1038/s42256-025-01018-6","url":null,"abstract":"Artificial intelligence-powered wearable electronic systems offer promising solutions for non-invasive visual assistance. However, state-of-the-art systems have not sufficiently considered human adaptation, resulting in a low adoption rate among blind people. Here we present a human-centred, multimodal wearable system that advances usability by blending software and hardware innovations. For software, we customize the artificial intelligence algorithm to match the requirements of application scenario and human behaviours. For hardware, we improve the wearability by developing stretchable sensory-motor artificial skins to complement the audio feedback and visual tasks. Self-powered triboelectric smart insoles align real users with virtual avatars, supporting effective training in carefully designed scenarios. The harmonious corporation of visual, audio and haptic senses enables significant improvements in navigation and postnavigation tasks, which are experimentally evidenced by humanoid robots and participants with visual impairment in both virtual and real environments. Postexperiment surveys highlight the system’s reliable functionality and high usability. This research paves the way for user-friendly visual assistance systems, offering alternative avenues to enhance the quality of life for people with visual impairment.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"38 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143832515","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}

引用次数: 0

A predictive machine learning force-field framework for liquid electrolyte development 液体电解质开发的预测机器学习力场框架

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-04-01 DOI: 10.1038/s42256-025-01009-7

Sheng Gong, Yumin Zhang, Zhenliang Mu, Zhichen Pu, Hongyi Wang, Xu Han, Zhiao Yu, Mengyi Chen, Tianze Zheng, Zhi Wang, Lifei Chen, Zhenze Yang, Xiaojie Wu, Shaochen Shi, Weihao Gao, Wen Yan, Liang Xiang

{"title":"A predictive machine learning force-field framework for liquid electrolyte development","authors":"Sheng Gong, Yumin Zhang, Zhenliang Mu, Zhichen Pu, Hongyi Wang, Xu Han, Zhiao Yu, Mengyi Chen, Tianze Zheng, Zhi Wang, Lifei Chen, Zhenze Yang, Xiaojie Wu, Shaochen Shi, Weihao Gao, Wen Yan, Liang Xiang","doi":"10.1038/s42256-025-01009-7","DOIUrl":"https://doi.org/10.1038/s42256-025-01009-7","url":null,"abstract":"Despite the widespread applications of machine learning force fields (MLFFs) in solids and small molecules, there is a notable gap in applying MLFFs to simulate liquid electrolytes—a critical component of current commercial lithium-ion batteries. Here we introduce ByteDance Artificial intelligence Molecular simulation Booster (BAMBOO), a predictive framework for molecular dynamics simulations, with a demonstration of its capability in the context of liquid electrolytes for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the backbone of BAMBOO to learn from quantum mechanical simulations. Additionally, we introduce an ensemble knowledge distillation approach and apply it to MLFFs to reduce the fluctuation of observations from molecular dynamics simulations. Finally, we propose a density alignment algorithm to align BAMBOO with experimental measurements. BAMBOO demonstrates state-of-the-art accuracy in predicting key electrolyte properties such as density, viscosity and ionic conductivity across various solvents and salt combinations. The current model, trained on more than 15 chemical species, achieves an average density error of 0.01 g cm−3 on various compositions compared with experiment.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"55 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744769","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}

引用次数: 0

InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments InstaNovo能够在大规模蛋白质组学实验中进行扩散驱动的从头肽测序

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-03-31 DOI: 10.1038/s42256-025-01019-5

Kevin Eloff, Konstantinos Kalogeropoulos, Amandla Mabona, Oliver Morell, Rachel Catzel, Esperanza Rivera-de-Torre, Jakob Berg Jespersen, Wesley Williams, Sam P. B. van Beljouw, Marcin J. Skwark, Andreas Hougaard Laustsen, Stan J. J. Brouns, Anne Ljungars, Erwin M. Schoof, Jeroen Van Goey, Ulrich auf dem Keller, Karim Beguir, Nicolas Lopez Carranza, Timothy P. Jenkins

{"title":"InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments","authors":"Kevin Eloff, Konstantinos Kalogeropoulos, Amandla Mabona, Oliver Morell, Rachel Catzel, Esperanza Rivera-de-Torre, Jakob Berg Jespersen, Wesley Williams, Sam P. B. van Beljouw, Marcin J. Skwark, Andreas Hougaard Laustsen, Stan J. J. Brouns, Anne Ljungars, Erwin M. Schoof, Jeroen Van Goey, Ulrich auf dem Keller, Karim Beguir, Nicolas Lopez Carranza, Timothy P. Jenkins","doi":"10.1038/s42256-025-01019-5","DOIUrl":"https://doi.org/10.1038/s42256-025-01019-5","url":null,"abstract":"Mass spectrometry-based proteomics focuses on identifying the peptide that generates a tandem mass spectrum. Traditional methods rely on protein databases but are often limited or inapplicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to spectra without prior information, is valuable for diverse biological applications; however, owing to a lack of accuracy, it remains challenging to apply. Here we introduce InstaNovo, a transformer model that translates fragment ion peaks into peptide sequences. We demonstrate that InstaNovo outperforms state-of-the-art methods and showcase its utility in several applications. We also introduce InstaNovo+, a diffusion model that improves performance through iterative refinement of predicted sequences. Using these models, we achieve improved therapeutic sequencing coverage, discover novel peptides and detect unreported organisms in diverse datasets, thereby expanding the scope and detection rate of proteomics searches. Our models unlock opportunities across domains such as direct protein sequencing, immunopeptidomics and exploration of the dark proteome.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"69 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736615","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}

引用次数: 0