Nature Machine Intelligence最新文献

Learning to adapt through bio-inspired gait strategies for versatile quadruped locomotion 学习通过仿生步态策略适应多用途四足运动

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-07-11 DOI: 10.1038/s42256-025-01065-z

Joseph Humphreys, Chengxu Zhou

引用次数: 0

A predictive framework for liquid electrolytes takes root with BAMBOO 液体电解质的预测框架在BAMBOO中生根发芽

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-07-10 DOI: 10.1038/s42256-025-01071-1

Ioan-Bogdan Magdău, Gábor Csányi

引用次数: 0

Self-iterative multiple-instance learning enables the prediction of CD4+ T cell immunogenic epitopes 自迭代多实例学习能够预测CD4+ T细胞免疫原性表位

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-07-10 DOI: 10.1038/s42256-025-01073-z

Long-Chen Shen, Yumeng Zhang, Zhikang Wang, Dene R. Littler, Yan Liu, Jinhui Tang, Jamie Rossjohn, Dong-Jun Yu, Jiangning Song

{"title":"Self-iterative multiple-instance learning enables the prediction of CD4+ T cell immunogenic epitopes","authors":"Long-Chen Shen, Yumeng Zhang, Zhikang Wang, Dene R. Littler, Yan Liu, Jinhui Tang, Jamie Rossjohn, Dong-Jun Yu, Jiangning Song","doi":"10.1038/s42256-025-01073-z","DOIUrl":"https://doi.org/10.1038/s42256-025-01073-z","url":null,"abstract":"Accurate prediction of antigen presentation to CD4+ T cells and subsequent induction of immune response are fundamentally important for vaccine development, autoimmune disease treatment and cancer neoepitope discovery. In immunopeptidomics, single-allelic data offer high specificity but limited allele coverage, whereas multi-allelic data provide broader representation at the expense of weak labelling. Current computational approaches either overlook the abundance of multi-allelic data or suffer from label ambiguity due to inadequate modelling strategies. To address these limitations, we present ImmuScope, a weakly supervised deep learning framework that integrates major histocompatibility complex class II (MHC-II) antigen presentation, CD4+ T cell epitopes and immunogenicity assessment. ImmuScope leverages self-iterative multiple-instance learning with positive-anchor triplet loss to decipher peptide-MHC-II binding from weakly labelled multi-allelic data and high-confidence single-allelic data. The training dataset comprises over 600,000 ligands across 142 alleles. Additionally, ImmuScope enables the interpretation of MHC-II binding specificity and motif deconvolution of immunopeptidomics data. We successfully applied ImmuScope to identify melanoma neoantigens, uncovering mutation-driven variations in peptide-MHC-II binding and immunogenicity. Furthermore, we employed ImmuScope to evaluate the effects of SARS-CoV-2 epitope mutations associated with immune escape, with predictions well aligned with experimentally observed immune escape dynamics. Overall, by offering a unified solution for CD4+ T cell antigen recognition and immunogenicity assessment, ImmuScope holds substantial promise for accelerating vaccine design and advancing personalized immunotherapy.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"91 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594135","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

Enabling large language models for real-world materials discovery 为现实世界的材料发现启用大型语言模型

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-07-10 DOI: 10.1038/s42256-025-01058-y

Santiago Miret, N. M. Anoop Krishnan

{"title":"Enabling large language models for real-world materials discovery","authors":"Santiago Miret, N. M. Anoop Krishnan","doi":"10.1038/s42256-025-01058-y","DOIUrl":"https://doi.org/10.1038/s42256-025-01058-y","url":null,"abstract":"Large language models (LLMs) create exciting possibilities to accelerate scientific discovery and knowledge dissemination in materials science. While LLMs have been successfully applied to select scientific problems and rudimentary challenges, they currently fall short of being practical materials science tools. In this Perspective, we show relevant failure cases of LLMs in materials science that reveal the current limitations of LLMs related to comprehending and reasoning over complex, interconnected materials science knowledge. Given these shortcomings, we outline a framework for developing materials science LLMs (MatSci-LLMs) that are grounded in domain knowledge, which can enable hypothesis generation followed by hypothesis testing for impactful materials science challenges. The path to attaining performant MatSci-LLMs rests, in large part, on building high-quality, multimodal datasets sourced from scientific literature, where various information extraction challenges persist. As such, we describe key materials science information extraction challenges that need to be overcome to build large-scale, multimodal datasets that capture valuable materials science principles and broader knowledge.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"23 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594134","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

Large language models to accelerate organic chemistry synthesis 大型语言模型，加速有机化学合成

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-07-01 DOI: 10.1038/s42256-025-01066-y

Yu Zhang, Yang Han, Shuai Chen, Ruijie Yu, Xin Zhao, Xianbin Liu, Kaipeng Zeng, Mengdi Yu, Jidong Tian, Feng Zhu, Xiaokang Yang, Yaohui Jin, Yanyan Xu

{"title":"Large language models to accelerate organic chemistry synthesis","authors":"Yu Zhang, Yang Han, Shuai Chen, Ruijie Yu, Xin Zhao, Xianbin Liu, Kaipeng Zeng, Mengdi Yu, Jidong Tian, Feng Zhu, Xiaokang Yang, Yaohui Jin, Yanyan Xu","doi":"10.1038/s42256-025-01066-y","DOIUrl":"https://doi.org/10.1038/s42256-025-01066-y","url":null,"abstract":"Chemical synthesis, as a foundational methodology in the creation of transformative molecules, exerts substantial influence across diverse sectors from life sciences to materials and energy. Current chemical synthesis practices emphasize laborious and costly trial-and-error workflows, underscoring the urgent needs for advanced AI assistants. Recently, large language models, typified by GPT-4, have been introduced as an efficient tool to facilitate scientific research. Here we present Chemma, a fully fine-tuned large language model with 1.28 million pairs of questions and answers about reactions, as an assistant to accelerate organic chemistry synthesis. Chemma surpasses the best-known results in multiple chemical tasks, for example, single-step retrosynthesis and yield prediction, which highlights the potential of general artificial intelligence for organic chemistry. By predicting yields across the experimental reaction space, Chemma significantly improves the reaction exploration capability of Bayesian optimization. More importantly, integrated in an active learning framework, Chemma exhibits advanced potentials of autonomously experimental exploration and optimization in open reaction spaces. For an unreported Suzuki–Miyaura cross-coupling reaction of cyclic aminoboronates and aryl halides for the synthesis of α-aryl N-heterocycles, the human–artificial intelligence collaboration successfully explored a suitable ligand (tri(1-adamantyl)phosphine) and solvent (1,4-dioxane) within only 15 runs, achieving an isolated yield of 67%. These results reveal that, without quantum-chemical calculations, Chemma can comprehend and extract chemical insights from reaction data, in a manner akin to human experts. This work opens avenues for accelerating organic chemistry synthesis with adapted large language models.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"41 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520683","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

Model-based reinforcement learning for ultrasound-driven autonomous microrobots 基于模型的超声驱动自主微型机器人强化学习

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-06-26 DOI: 10.1038/s42256-025-01054-2

Mahmoud Medany, Lorenzo Piglia, Liam Achenbach, S. Karthik Mukkavilli, Daniel Ahmed

{"title":"Model-based reinforcement learning for ultrasound-driven autonomous microrobots","authors":"Mahmoud Medany, Lorenzo Piglia, Liam Achenbach, S. Karthik Mukkavilli, Daniel Ahmed","doi":"10.1038/s42256-025-01054-2","DOIUrl":"https://doi.org/10.1038/s42256-025-01054-2","url":null,"abstract":"Reinforcement learning is emerging as a powerful tool for microrobots control, as it enables autonomous navigation in environments where classical control approaches fall short. However, applying reinforcement learning to microrobotics is difficult due to the need for large training datasets, the slow convergence in physical systems and poor generalizability across environments. These challenges are amplified in ultrasound-actuated microrobots, which require rapid, precise adjustments in high-dimensional action space, which are often too complex for human operators. Addressing these challenges requires sample-efficient algorithms that adapt from limited data while managing complex physical interactions. To meet these challenges, we implemented model-based reinforcement learning for autonomous control of an ultrasound-driven microrobot, which learns from recurrent imagined environments. Our non-invasive, AI-controlled microrobot offers precise propulsion and efficiently learns from images in data-scarce environments. On transitioning from a pretrained simulation environment, we achieved sample-efficient collision avoidance and channel navigation, reaching a 90% success rate in target navigation across various channels within an hour of fine-tuning. Moreover, our model initially generalized successfully in 50% of tasks in new environments, improving to over 90% with 30‚Äâmin of further training. We further demonstrated real-time manipulation of microrobots in complex vasculatures under both static and flow conditions, thus underscoring the potential of AI to revolutionize microrobotics in biomedical applications.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"46 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144488385","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

Universal interatomic potentials shine in finding crystal structures 普遍的原子间相互作用势在寻找晶体结构方面大放异彩

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-06-26 DOI: 10.1038/s42256-025-01061-3

Ju Li

引用次数: 0

End-to-end cryo-EM complex structure determination with high accuracy and ultra-fast speed 端到端低温电镜复杂结构测定，高精度，超快速度

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-06-24 DOI: 10.1038/s42256-025-01056-0

Jue Wang, Cheng Tan, Zhangyang Gao, Guijun Zhang, Yang Zhang, Stan Z. Li

{"title":"End-to-end cryo-EM complex structure determination with high accuracy and ultra-fast speed","authors":"Jue Wang, Cheng Tan, Zhangyang Gao, Guijun Zhang, Yang Zhang, Stan Z. Li","doi":"10.1038/s42256-025-01056-0","DOIUrl":"https://doi.org/10.1038/s42256-025-01056-0","url":null,"abstract":"While cryogenic-electron microscopy yields high-resolution density maps for complex structures, accurate determination of the corresponding atomic structures still necessitates significant expertise and labour-intensive manual interpretation. Recently, artificial intelligence-based methods have emerged to streamline this process; however, several challenges persist. First, existing methods typically require multi-stage training and inference, causing inefficiencies and inconsistency. Second, these approaches often encounter bias and incur substantial computational costs in aligning predicted atomic coordinates with sequence. Last, due to the limitations of available datasets, previous studies struggle to generalize effectively to complicated and unseen test data. Here, in response to these challenges, we introduce end-to-end and efficient CryoFold (E3-CryoFold), a deep learning method that enables end-to-end training and one-shot inference. E3-CryoFold uses three-dimensional and sequence transformers to extract features from density maps and sequences, using cross-attention modules to integrate the two modalities. Additionally, it uses an SE(3) graph neural network to construct atomic structures based on extracted features. E3-CryoFold incorporates a pretraining stage, during which models are trained on simulated density maps derived from Protein Data Bank structures. Empirical results demonstrate that E3-CryoFold improves the average template modelling score of the generated structures by 400% as compared to Cryo2Struct and significantly outperforms ModelAngelo, while achieving this huge improvement using merely one-thousandth of the inference time required by these methods. Thus, E3-CryoFold represents a robust, streamlined and cohesive framework for cryogenic-electron microscopy structure determination.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"41 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144371011","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

Dimensions underlying the representational alignment of deep neural networks with humans 深层神经网络与人类的表征对齐的维度

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-06-23 DOI: 10.1038/s42256-025-01041-7

Florian P. Mahner, Lukas Muttenthaler, Umut Güçlü, Martin N. Hebart

{"title":"Dimensions underlying the representational alignment of deep neural networks with humans","authors":"Florian P. Mahner, Lukas Muttenthaler, Umut Güçlü, Martin N. Hebart","doi":"10.1038/s42256-025-01041-7","DOIUrl":"https://doi.org/10.1038/s42256-025-01041-7","url":null,"abstract":"Determining the similarities and differences between humans and artificial intelligence (AI) is an important goal in both computational cognitive neuroscience and machine learning, promising a deeper understanding of human cognition and safer, more reliable AI systems. Much previous work comparing representations in humans and AI has relied on global, scalar measures to quantify their alignment. However, without explicit hypotheses, these measures only inform us about the degree of alignment, not the factors that determine it. To address this challenge, we propose a generic framework to compare human and AI representations, based on identifying latent representational dimensions underlying the same behaviour in both domains. Applying this framework to humans and a deep neural network (DNN) model of natural images revealed a low-dimensional DNN embedding of both visual and semantic dimensions. In contrast to humans, DNNs exhibited a clear dominance of visual over semantic properties, indicating divergent strategies for representing images. Although in silico experiments showed seemingly consistent interpretability of DNN dimensions, a direct comparison between human and DNN representations revealed substantial differences in how they process images. By making representations directly comparable, our results reveal important challenges for representational alignment and offer a means for improving their comparability.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"12 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340984","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 process-centric manipulation taxonomy for the organization, classification and synthesis of tactile robot skills 一个以过程为中心的操作分类法，用于触觉机器人技能的组织、分类和综合

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-06-23 DOI: 10.1038/s42256-025-01045-3

Lars Johannsmeier, Samuel Schneider, Yanan Li, Etienne Burdet, Sami Haddadin

{"title":"A process-centric manipulation taxonomy for the organization, classification and synthesis of tactile robot skills","authors":"Lars Johannsmeier, Samuel Schneider, Yanan Li, Etienne Burdet, Sami Haddadin","doi":"10.1038/s42256-025-01045-3","DOIUrl":"https://doi.org/10.1038/s42256-025-01045-3","url":null,"abstract":"Despite decades of research in robotic manipulation, only a few autonomous manipulation skills are currently used. Traditional and machine-learning-based end-to-end solutions have shown substantial progress but still struggle to generate reliable manipulation skills for difficult processes like insertion or bending material. To facilitate the deployment and learning of tactile robot manipulation skills, we introduce here a taxonomy based on formal process specifications provided by experts, which assigns a suitable skill to a given process. We validated the inherent scalability of the taxonomy on 28 different skills from industrial application domains. The experimental results had success rates close to 100%, even under goal pose disturbances, with high performance attained by the skill models in terms of execution times and contact moments in partially known environments. The basic elements of the models are reusable and facilitate skill-learning to optimize control performance. Like established curricula for human trainees, this framework could provide a comprehensive platform that enables robots to acquire relevant manipulation skills and act as a catalyst to propel automation beyond its current capabilities.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"18 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340813","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