Nature Machine Intelligence最新文献

Deep spectral component filtering as a foundation model for spectral analysis demonstrated in metabolic profiling 深光谱分量滤波是代谢谱分析的基础模型

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-23 DOI: 10.1038/s42256-025-01027-5

Bingsen Xue, Xinyuan Bi, Zheyi Dong, Yunzhe Xu, Minghui Liang, Xin Fang, Yizhe Yuan, Ruoxi Wang, Shuyu Liu, Rushi Jiao, Yuze Chen, Weitao Zu, Chengxiang Wang, Jianhao Zhang, Jiang Liu, Qin Zhang, Ye Yuan, Midie Xu, Ya Zhang, Yanfeng Wang, Jian Ye, Cheng Jin

{"title":"Deep spectral component filtering as a foundation model for spectral analysis demonstrated in metabolic profiling","authors":"Bingsen Xue, Xinyuan Bi, Zheyi Dong, Yunzhe Xu, Minghui Liang, Xin Fang, Yizhe Yuan, Ruoxi Wang, Shuyu Liu, Rushi Jiao, Yuze Chen, Weitao Zu, Chengxiang Wang, Jianhao Zhang, Jiang Liu, Qin Zhang, Ye Yuan, Midie Xu, Ya Zhang, Yanfeng Wang, Jian Ye, Cheng Jin","doi":"10.1038/s42256-025-01027-5","DOIUrl":"https://doi.org/10.1038/s42256-025-01027-5","url":null,"abstract":"Analysing metabolites in bioliquids through various spectroscopic methods provides valuable insights into the metabolic phenotypes. Deciphering spectral data has greatly benefited from deep-learning methods; however, data-driven solutions often struggle with data dependence on different devices, samples and spectral modalities. Most current task-specific methods have limited generalizability to different spectral analysis problems, including preprocessing, quantification and interpretation. Here, we developed a pretrained foundation model, termed deep-spectral component filtering (DSCF) through a self-supervised approach termed spectral component resolvable learning. By acquiring general spectral knowledge, DSCF achieved state-of-the-art performance for five distinct spectral analysis tasks on 11 datasets. Notably, the general pretraining led to zero-shot spectral denoising and trace-level quantification in complex mixtures. DSCF achieved molecule-level interpretation of surface-enhanced Raman spectra and mapped serum metabolic profiles from nearly 600 individuals for various diseases, including stroke, Alzheimer’s disease and prostate cancer. Overall, the proposed foundation model illustrates promising generalizability for spectral analysis and offers a clear and feasible pathway for general spectral analysis.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"31 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123090","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

Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs 利用注入立体电子的分子图推进分子机器学习表征

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-23 DOI: 10.1038/s42256-025-01031-9

Daniil A. Boiko, Thiago Reschützegger, Benjamin Sanchez-Lengeling, Samuel M. Blau, Gabe Gomes

{"title":"Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs","authors":"Daniil A. Boiko, Thiago Reschützegger, Benjamin Sanchez-Lengeling, Samuel M. Blau, Gabe Gomes","doi":"10.1038/s42256-025-01031-9","DOIUrl":"https://doi.org/10.1038/s42256-025-01031-9","url":null,"abstract":"Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learning models have used strings, fingerprints, global features and simple molecular graphs that are inherently information-sparse representations. However, as the complexity of prediction tasks increases, the molecular representation needs to encode higher fidelity information. This work introduces a new approach to infusing quantum-chemical-rich information into molecular graphs via stereoelectronic effects, enhancing expressivity and interpretability. Learning to predict the stereoelectronics-infused representation with a tailored double graph neural network workflow enables its application to any downstream molecular machine learning task without expensive quantum-chemical calculations. We show that the explicit addition of stereoelectronic information substantially improves the performance of message-passing two-dimensional machine learning models for molecular property prediction. We show that the learned representations trained on small molecules can accurately extrapolate to much larger molecular structures, yielding chemical insight into orbital interactions for previously intractable systems, such as entire proteins, opening new avenues of molecular design. Finally, we have developed a web application (simg.cheme.cmu.edu) where users can rapidly explore stereoelectronic information for their own molecular systems.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"15 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130227","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

Firefighting robots should be made responsibly 消防机器人应该是负责任的

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-23 DOI: 10.1038/s42256-025-01037-3

Benjamin R. van Manen, Eduard Fosch-Villaronga, Merlijn Smits

{"title":"Firefighting robots should be made responsibly","authors":"Benjamin R. van Manen, Eduard Fosch-Villaronga, Merlijn Smits","doi":"10.1038/s42256-025-01037-3","DOIUrl":"https://doi.org/10.1038/s42256-025-01037-3","url":null,"abstract":"Firefighting robots have the potential to transform emergency response by operating in hazardous environments that pose extreme risks to firefighters. These ‘firebots’ can withstand extreme heat, smoke and structural instability without fatigue. However, experienced firefighters rely on intuition, teamwork and years of practice to navigate these dangers. Scepticism toward innovation in high-stakes environments is expected, and if poorly integrated, firebots could indeed hinder rather than enhance safety. To ensure that firebots complement rather than disrupt life-saving operations, we argue for a responsible design framework that aligns their development with firefighter values and decision-making processes.Firefighting is an extremely hazardous profession, exposing firefighters to intense heat, toxic smoke and the risk of structural collapse. Operating in low-visibility conditions, they must make split-second decisions with limited information while enduring severe physical and psychological stress. These challenges not only increase the immediate risk of injury or death but also may contribute to long-term health consequences, such as cancer. Recognizing these risks, fire departments have long explored technology to enhance safety and efficiency. Teleoperated firefighting robots are increasingly used to provide situational awareness and execute basic tasks such as towing and fire suppression. However, they require substantial human oversight, diverting attention from critical life-saving actions. As a result, there is growing interest in advancing robotic autonomy to enable a more seamless human–robot collaboration, in which AI not merely assists but also provides decision-making support in high-risk environments. However, increased autonomy introduces new risks, particularly if firebots fail in unpredictable, life-threatening conditions without robust safety mechanisms and oversight.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"97 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130226","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

Localizing AI in the global south 在全球南部地区进行人工智能本地化

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-21 DOI: 10.1038/s42256-025-01057-z

引用次数: 0

A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics 一个个性化的时间分辨三维网格生成模型揭示正常的心脏动力学

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-19 DOI: 10.1038/s42256-025-01035-5

Mengyun Qiao, Kathryn A. McGurk, Shuo Wang, Paul M. Matthews, Declan P. O’Regan, Wenjia Bai

{"title":"A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics","authors":"Mengyun Qiao, Kathryn A. McGurk, Shuo Wang, Paul M. Matthews, Declan P. O’Regan, Wenjia Bai","doi":"10.1038/s42256-025-01035-5","DOIUrl":"https://doi.org/10.1038/s42256-025-01035-5","url":null,"abstract":"Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in cardiac shape and motion patterns, influenced by demographic, anthropometric and disease factors. Unravelling normal patterns of shape and motion, and understanding how each individual deviates from the norm, would facilitate accurate diagnosis and personalized treatment strategies. Here, to this end, we developed a conditional generative model, MeshHeart, to learn the distribution of shape and motion patterns for the left and right ventricles of the heart. To model the high-dimensional spatio-temporal mesh data, MeshHeart uses a geometric encoder to represent cardiac meshes in a latent space and a temporal transformer to model the motion dynamics of latent representations. Based on MeshHeart, we investigate the latent space of 3D + t cardiac mesh sequences and propose a distance metric, latent delta, which quantifies the deviation of a real heart from its personalized normative pattern. Here, 3D + t refers to three-dimensional data evolving over time. In experiments using a large cardiac magnetic resonance image dataset of 38,309 participants from the UK Biobank, MeshHeart demonstrates high performance in cardiac mesh sequence reconstruction and generation. Latent space features are discriminative for cardiac disease classification, whereas latent delta exhibits strong correlations with clinical phenotypes in phenome-wide association studies.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"31 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088333","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

Compositional pretraining improves computational efficiency and matches animal behaviour on complex tasks 组合预训练提高了计算效率，并在复杂任务中匹配动物行为

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-19 DOI: 10.1038/s42256-025-01029-3

David Hocker, Christine M. Constantinople, Cristina Savin

{"title":"Compositional pretraining improves computational efficiency and matches animal behaviour on complex tasks","authors":"David Hocker, Christine M. Constantinople, Cristina Savin","doi":"10.1038/s42256-025-01029-3","DOIUrl":"https://doi.org/10.1038/s42256-025-01029-3","url":null,"abstract":"Recurrent neural networks (RNNs) are ubiquitously used in neuroscience to capture both neural dynamics and behaviours of living systems. However, when it comes to complex cognitive tasks, training RNNs with traditional methods can prove difficult and fall short of capturing crucial aspects of animal behaviour. Here we propose a principled approach for identifying and incorporating compositional tasks as part of RNN training. Taking as the target a temporal wagering task previously studied in rats, we design a pretraining curriculum of simpler cognitive tasks that reflect relevant subcomputations, which we term ‘kindergarten curriculum learning’. We show that this pretraining substantially improves learning efficacy and is critical for RNNs to adopt similar strategies as rats, including long-timescale inference of latent states, which conventional pretraining approaches fail to capture. Mechanistically, our pretraining supports the development of slow dynamical systems features needed for implementing both inference and value-based decision making. Overall, our approach helps endow RNNs with relevant inductive biases, which is important when modelling complex behaviours that rely on multiple cognitive functions.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"40 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088332","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

Back to recurrent processing at the crossroad of transformers and state-space models 回到变压器和状态空间模型交叉路口的循环处理

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-15 DOI: 10.1038/s42256-025-01034-6

Matteo Tiezzi, Michele Casoni, Alessandro Betti, Tommaso Guidi, Marco Gori, Stefano Melacci

引用次数: 0

Bridging chemistry and artificial intelligence by a reaction description language 用反应描述语言架起化学与人工智能的桥梁

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-13 DOI: 10.1038/s42256-025-01032-8

Jiacheng Xiong, Wei Zhang, Yinquan Wang, Jiatao Huang, Yuqi Shi, Mingyan Xu, Manjia Li, Zunyun Fu, Xiangtai Kong, Yitian Wang, Zhaoping Xiong, Mingyue Zheng

{"title":"Bridging chemistry and artificial intelligence by a reaction description language","authors":"Jiacheng Xiong, Wei Zhang, Yinquan Wang, Jiatao Huang, Yuqi Shi, Mingyan Xu, Manjia Li, Zunyun Fu, Xiangtai Kong, Yitian Wang, Zhaoping Xiong, Mingyue Zheng","doi":"10.1038/s42256-025-01032-8","DOIUrl":"https://doi.org/10.1038/s42256-025-01032-8","url":null,"abstract":"With the fast-paced development of artificial intelligence, large language models are increasingly used to tackle various scientific challenges. A critical step in this process is converting domain-specific data into a sequence of tokens for language modelling. In chemistry, molecules are often represented by molecular linear notations, and chemical reactions are depicted as sequence pairs of reactants and products. However, this approach does not capture atomic and bond changes during reactions. Here, we present ReactSeq, a reaction description language that defines molecular editing operations for step-by-step chemical transformation. Based on ReactSeq, language models for retrosynthesis prediction may consistently excel in all benchmark tests, and demonstrate promising emergent abilities in the human-in-the-loop and explainable artificial intelligence. Moreover, ReactSeq has allowed us to obtain universal and reliable representations of chemical reactions, which enable navigation of the reaction space and aid in the recommendation of experimental procedures and prediction of reaction yields. We foresee that ReactSeq can serve as a bridge to narrow the gap between chemistry and artificial intelligence.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"3 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940633","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

Generating 3D small binding molecules using shape-conditioned diffusion models with guidance 生成三维小结合分子使用形状条件扩散模型与指导

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-12 DOI: 10.1038/s42256-025-01030-w

Ziqi Chen, Bo Peng, Tianhua Zhai, Daniel Adu-Ampratwum, Xia Ning

{"title":"Generating 3D small binding molecules using shape-conditioned diffusion models with guidance","authors":"Ziqi Chen, Bo Peng, Tianhua Zhai, Daniel Adu-Ampratwum, Xia Ning","doi":"10.1038/s42256-025-01030-w","DOIUrl":"https://doi.org/10.1038/s42256-025-01030-w","url":null,"abstract":"Drug development is a critical but notoriously resource- and time-consuming process. Traditional methods, such as high-throughput screening, rely on opportunistic trial and error and cannot ensure optimal precision design. To overcome these challenges, generative artificial intelligence methods have emerged to directly design molecules with desired properties. Here we develop a generative artificial intelligence method DiffSMol for drug discovery that generates 3D small binding molecules based on known ligand shapes. DiffSMol encapsulates ligand shape details within pretrained, expressive shape embeddings and generates binding molecules through a diffusion model. DiffSMol further modifies the generated 3D structures iteratively using shape guidance to better resemble ligand shapes, and protein pocket guidance to optimize binding affinities. We show that DiffSMol outperforms state-of-the-art methods on benchmark datasets. When generating binding molecules resembling ligand shapes, DiffSMol with shape guidance achieves a success rate 61.4%, substantially outperforming the best baseline (11.2%), meanwhile producing molecules with de novo graph structures. DiffSMol with pocket guidance also outperforms the best baseline in binding affinities by 13.2%, and even by 17.7% when combined with shape guidance. Case studies for two critical drug targets demonstrate very favourable physicochemical and pharmacokinetic properties of generated molecules, highlighting the potential of DiffSMol in developing promising drug candidates.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"27 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933591","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

Lossless data compression by large models 大型模型的无损数据压缩

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-05-01 DOI: 10.1038/s42256-025-01033-7

Ziguang Li, Chao Huang, Xuliang Wang, Haibo Hu, Cole Wyeth, Dongbo Bu, Quan Yu, Wen Gao, Xingwu Liu, Ming Li

引用次数: 0