Nature Machine Intelligence最新文献

{"title":"Image-based generation for molecule design with SketchMol","authors":"Zixu Wang, Yangyang Chen, Pengsen Ma, Zhou Yu, Jianmin Wang, Yuansheng Liu, Xiucai Ye, Tetsuya Sakurai, Xiangxiang Zeng","doi":"10.1038/s42256-025-00982-3","DOIUrl":"10.1038/s42256-025-00982-3","url":null,"abstract":"Efficient molecular design methods are crucial for accelerating early stage drug discovery, potentially saving years of development time and billions of dollars in costs. Current molecular design methods rely on sequence-based or graph-based representations, emphasizing local features such as bonds and atoms but lacking a comprehensive depiction of the overall molecular topology. Here we introduce SketchMol, an image-based molecular generation framework that combines visual understanding with molecular design. SketchMol leverages diffusion models and applies a refinement technique called reinforcement learning from molecular experts to improve the generation of viable molecules. It creates molecules through a painting-like approach that simultaneously depicts local structures and global layout of the molecule. By visualizing molecular structures, various design tasks are unified within a single image-based framework. De novo design becomes sketching new molecular images, whereas editing tasks transform into filling partially drawn images. Through extensive experiments, we demonstrated that SketchMol effectively handles a variety of molecular design tasks. SketchMol is a model that explores the feasibility of incorporating image generation techniques into the field of small-molecule design.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"244-255"},"PeriodicalIF":18.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401246","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}

{"title":"On board with COMET to improve omics prediction models","authors":"Paul Fogel, George Luta","doi":"10.1038/s42256-025-00990-3","DOIUrl":"10.1038/s42256-025-00990-3","url":null,"abstract":"The performance of omics prediction models can be significantly improved by combining limited patient proteomic data with widely available electronic health records.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"168-169"},"PeriodicalIF":18.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143393225","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}

{"title":"On the caveats of AI autophagy","authors":"Xiaodan Xing, Fadong Shi, Jiahao Huang, Yinzhe Wu, Yang Nan, Sheng Zhang, Yingying Fang, Michael Roberts, Carola-Bibiane Schönlieb, Javier Del Ser, Guang Yang","doi":"10.1038/s42256-025-00984-1","DOIUrl":"10.1038/s42256-025-00984-1","url":null,"abstract":"Generative artificial intelligence (AI) technologies and large models are producing realistic outputs across various domains, such as images, text, speech and music. Creating these advanced generative models requires significant resources, particularly large and high-quality datasets. To minimize training expenses, many algorithm developers use data created by the models themselves as a cost-effective training solution. However, not all synthetic data effectively improve model performance, necessitating a strategic balance in the use of real versus synthetic data to optimize outcomes. Currently, the previously well-controlled integration of real and synthetic data is becoming uncontrollable. The widespread and unregulated dissemination of synthetic data online leads to the contamination of datasets traditionally compiled through web scraping, now mixed with unlabelled synthetic data. This trend, known as the AI autophagy phenomenon, suggests a future where generative AI systems may increasingly consume their own outputs without discernment, raising concerns about model performance, reliability and ethical implications. What will happen if generative AI continuously consumes itself without discernment? What measures can we take to mitigate the potential adverse effects? To address these research questions, this Perspective examines the existing literature, delving into the consequences of AI autophagy, analysing the associated risks and exploring strategies to mitigate its impact. Our aim is to provide a comprehensive perspective on this phenomenon advocating for a balanced approach that promotes the sustainable development of generative AI technologies in the era of large models. With widespread generation and availability of synthetic data, AI systems are increasingly trained on their own outputs, leading to various technical and ethical challenges. The authors analyse this development and discuss measures to mitigate the potential adverse effects of ‘AI eating itself’.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"172-180"},"PeriodicalIF":18.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143375174","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}

{"title":"The promise of generative AI for suicide prevention in India","authors":"Tanmoy Chakraborty, Koushik Sinha Deb, Himanshu Kulkarni, Sarah Masud, Suresh Bada Math, Gayatri Oke, Rajesh Sagar, Mona Sharma","doi":"10.1038/s42256-025-00992-1","DOIUrl":"10.1038/s42256-025-00992-1","url":null,"abstract":"","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"162-163"},"PeriodicalIF":18.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191830","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}

{"title":"Discovering fully semantic representations via centroid- and orientation-aware feature learning","authors":"Jaehoon Cha, Jinhae Park, Samuel Pinilla, Kyle L. Morris, Christopher S. Allen, Mark I. Wilkinson, Jeyan Thiyagalingam","doi":"10.1038/s42256-024-00978-5","DOIUrl":"10.1038/s42256-024-00978-5","url":null,"abstract":"Learning meaningful representations of images in scientific domains that are robust to variations in centroids and orientations remains an important challenge. Here we introduce centroid- and orientation-aware disentangling autoencoder (CODAE), an encoder–decoder-based neural network that learns meaningful content of objects in a latent space. Specifically, a combination of a translation- and rotation-equivariant encoder, Euler encoding and an image moment loss enables CODAE to extract features invariant to positions and orientations of objects of interest from randomly translated and rotated images. We evaluate this approach on several publicly available scientific datasets, including protein images from life sciences, four-dimensional scanning transmission electron microscopy data from material science and galaxy images from astronomy. The evaluation shows that CODAE learns centroids, orientations and their invariant features and outputs, as well as aligned reconstructions and the exact view reconstructions of the input images with high quality. Cha and colleagues present a translation- and rotation-equivariant autoencoder-based method for robust image recognition, which they demonstrate on diverse tasks from bioinformatics, material science and astronomy.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"307-314"},"PeriodicalIF":18.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00978-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191831","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}

{"title":"Preserving and combining knowledge in robotic lifelong reinforcement learning","authors":"Yuan Meng, Zhenshan Bing, Xiangtong Yao, Kejia Chen, Kai Huang, Yang Gao, Fuchun Sun, Alois Knoll","doi":"10.1038/s42256-025-00983-2","DOIUrl":"10.1038/s42256-025-00983-2","url":null,"abstract":"Humans can continually accumulate knowledge and develop increasingly complex behaviours and skills throughout their lives, which is a capability known as ‘lifelong learning’. Although this lifelong learning capability is considered an essential mechanism that makes up general intelligence, recent advancements in artificial intelligence predominantly excel in narrow, specialized domains and generally lack this lifelong learning capability. Here we introduce a robotic lifelong reinforcement learning framework that addresses this gap by developing a knowledge space inspired by the Bayesian non-parametric domain. In addition, we enhance the agent’s semantic understanding of tasks by integrating language embeddings into the framework. Our proposed embodied agent can consistently accumulate knowledge from a continuous stream of one-time feeding tasks. Furthermore, our agent can tackle challenging real-world long-horizon tasks by combining and reapplying its acquired knowledge from the original tasks stream. The proposed framework advances our understanding of the robotic lifelong learning process and may inspire the development of more broadly applicable intelligence. Humans continuously acquire knowledge and develop complex behaviours. Meng, Bing, Yao and colleagues present a robotic lifelong learning framework using a Bayesian non-parametric knowledge space, enabling agents to dynamically preserve and integrate knowledge from sequential tasks, enhancing adaptability.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"256-269"},"PeriodicalIF":18.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-025-00983-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125412","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}

{"title":"Why the carbon footprint of generative large language models alone will not help us assess their sustainability","authors":"Leonie N. Bossert, Wulf Loh","doi":"10.1038/s42256-025-00979-y","DOIUrl":"10.1038/s42256-025-00979-y","url":null,"abstract":"There is a growing awareness of the substantial environmental costs of large language models (LLMs), but discussing the sustainability of LLMs only in terms of CO2 emissions is not enough. This Comment emphasizes the need to take into account the social and ecological costs and benefits of LLMs as well.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"164-165"},"PeriodicalIF":18.8,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077564","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}

{"title":"Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens","authors":"Jeremy Wohlwend, Anusha Nathan, Nitan Shalon, Charles R. Crain, Rhoda Tano-Menka, Benjamin Goldberg, Emma Richards, Gaurav D. Gaiha, Regina Barzilay","doi":"10.1038/s42256-024-00971-y","DOIUrl":"10.1038/s42256-024-00971-y","url":null,"abstract":"Accurate in silico determination of CD8+ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to complex biology, we curated a dataset of 651,237 unique human leukocyte antigen class I (HLA-I) ligands and developed MUNIS, a deep learning model that identifies peptides presented by HLA-I alleles. MUNIS shows improved performance compared with existing models in predicting peptide presentation and CD8+ T cell epitope immunodominance hierarchies. Moreover, application of MUNIS to proteins from Epstein–Barr virus led to successful identification of both established and novel HLA-I epitopes which were experimentally validated by in vitro HLA-I-peptide stability and T cell immunogenicity assays. MUNIS performs comparably to an experimental stability assay in terms of immunogenicity prediction, suggesting that deep learning can reduce experimental burden and accelerate identification of CD8+ T cell epitopes for rapid T cell vaccine development. Accurate prediction of immunogenic CD8+ T cell epitopes would greatly accelerate T cell vaccine development. A new deep learning model, MUNIS, can rapidly identify HLA-binding, immunogenic and immunodominant peptides in foreign pathogens.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"232-243"},"PeriodicalIF":18.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00971-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050094","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}

{"title":"A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules","authors":"Chenpeng Yu, Xing Fang, Shiye Tian, Hui Liu","doi":"10.1038/s42256-024-00973-w","DOIUrl":"10.1038/s42256-024-00973-w","url":null,"abstract":"The immune checkpoint inhibitors have demonstrated promising clinical efficacy across various tumour types, yet the percentage of patients who benefit from them remains low. The bindings between tumour antigens and human leukocyte antigen class I/T cell receptor molecules determine the antigen presentation and T cell activation, thereby playing an important role in the immunotherapy response. In this paper, we propose UnifyImmun, a unified cross-attention transformer model designed to simultaneously predict the bindings of peptides to both receptors, providing more comprehensive evaluation of antigen immunogenicity. We devise a two-phase strategy using virtual adversarial training that enables these two tasks to reinforce each other mutually, by compelling the encoders to extract more expressive features. Our method demonstrates superior performance in predicting both peptide-HLA and peptide-TCR binding on multiple independent and external test sets. Notably, on a large-scale COVID-19 peptide-TCR binding test set without any seen peptide in the training set, our method outperforms the current state-of-the-art methods by more than 10%. The predicted binding scores significantly correlate with the immunotherapy response and clinical outcomes on two clinical cohorts. Furthermore, the cross-attention scores and integrated gradients reveal the amino acid sites critical for peptide binding to receptors. In essence, our approach marks an essential step towards comprehensive evaluation of antigen immunogenicity. This work proposes a deep learning model based on the cross-attention mechanism to simultaneously predict peptide–HLA and peptide–TCR bindings. Experiments verify that its performance for both prediction tasks on multiple test sets compares favourably with previous methods.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 2","pages":"278-292"},"PeriodicalIF":18.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050075","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}

{"title":"Machine learning solutions looking for PDE problems","authors":"","doi":"10.1038/s42256-025-00989-w","DOIUrl":"10.1038/s42256-025-00989-w","url":null,"abstract":"Machine learning models are promising approaches to tackle partial differential equations, which are foundational descriptions of many scientific and engineering problems. However, in speaking with several experts about progress in the area, questions are emerging over what realistic advantages machine learning models have and how their performance should be evaluated.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 1","pages":"1-1"},"PeriodicalIF":18.8,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-025-00989-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044076","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}