Nature computational science最新文献

Translating biodiversity into chemical diversity. 将生物多样性转化为化学多样性。

IF 18.3

Nature computational science Pub Date : 2026-05-05 DOI: 10.1038/s43588-026-00984-0

引用次数: 0

DeepSeMS: revealing the hidden biosynthetic potential of the global ocean microbiome with a large language model. deepsem：通过大型语言模型揭示全球海洋微生物群隐藏的生物合成潜力。

IF 18.3

Nature computational science Pub Date : 2026-04-30 DOI: 10.1038/s43588-026-00983-1

Tingjun Xu, Yuwei Yang, Ruixin Zhu, Weili Lin, Jixuan Li, Yan Zheng, Peng Zhang, Guoqing Zhang, Guoping Zhao, Na Jiao

{"title":"DeepSeMS: revealing the hidden biosynthetic potential of the global ocean microbiome with a large language model.","authors":"Tingjun Xu, Yuwei Yang, Ruixin Zhu, Weili Lin, Jixuan Li, Yan Zheng, Peng Zhang, Guoqing Zhang, Guoping Zhao, Na Jiao","doi":"10.1038/s43588-026-00983-1","DOIUrl":"10.1038/s43588-026-00983-1","url":null,"abstract":"Microbial-derived secondary metabolites (SMs) hold great therapeutic potential but are predominantly discovered from cultured species, representing only a fraction of microbial biodiversity. Advances in metagenomics have unveiled reservoirs of biosynthetic gene clusters (BGCs), but translating genomic sequences into precise chemical structures remains challenging owing to the structural complexity of cryptic BGCs and the context-dependent substrate tolerance and cross-reactivity of modular biosynthetic domains. Here we present DeepSeMS, a transformer-based large language model that accurately predicts secondary metabolite chemical structures from BGC sequences. By encoding biosynthetic genes as functional domains and leveraging a feature-aligned data augmentation, DeepSeMS outperformed existing methods and successfully generated chemically valid predictions for 96.38% of cryptic BGCs. Applying DeepSeMS to a global ocean metagenome, we characterized over 60,000 secondary metabolites, revealing chemical diversity, ecological specificity and considerable biomedical potential, especially as antibiotics. This study underscores the capability of deep learning-driven approaches in revealing hidden biosynthetic potential of Earth's largest, yet largely unexplored, microbial ecosystem.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What reviewers request the most 评论者最要求的是什么

IF 18.3

Nature computational science Pub Date : 2026-04-28 DOI: 10.1038/s43588-026-00989-9

引用次数: 0

NOEM: efficient and scalable finite element method enabled by reusable neural operators NOEM：由可重复使用的神经算子实现的高效和可扩展的有限元方法

IF 18.3

Nature computational science Pub Date : 2026-04-28 DOI: 10.1038/s43588-026-00974-2

Weihang Ouyang, Yeonjong Shin, Si-Wei Liu, Lu Lu

{"title":"NOEM: efficient and scalable finite element method enabled by reusable neural operators","authors":"Weihang Ouyang, Yeonjong Shin, Si-Wei Liu, Lu Lu","doi":"10.1038/s43588-026-00974-2","DOIUrl":"10.1038/s43588-026-00974-2","url":null,"abstract":"The finite element method (FEM) is a well-established numerical method for solving partial differential equations (PDEs). However, its mesh-based nature gives rise to substantial computational costs, especially for complex multiscale simulations. Emerging machine learning-based methods provide data-driven solutions to PDEs, yet they present challenges, including high training cost and low model reusability. Here we propose the neural-operator element method (NOEM) by synergistically combining FEM with operator learning to address these challenges. NOEM leverages neural operators to simulate subdomains that require fine meshes in FEM. In each subdomain, a neural operator is used to build a single element, namely, a neural-operator element (NOE). NOEs are then integrated with standard finite elements to represent the entire solution through the variational framework. Thereby, NOEM does not necessitate dense meshing and offers efficient simulations. We demonstrate the accuracy, efficiency and scalability of NOEM by performing systematic theoretical analysis and numerical experiments, such as nonlinear PDEs, multiscale problems, PDEs on complex geometries and discontinuous coefficient fields. The authors propose a method that unifies finite element methods and machine learning by using neural operators as elements to model complex subdomains, yielding an efficient and scalable numerical framework with highly reusable machine learning models.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 4","pages":"417-429"},"PeriodicalIF":18.3,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147754495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Merging the classical and the modern in physics-based simulations 在基于物理的模拟中融合经典和现代

IF 18.3

Nature computational science Pub Date : 2026-04-28 DOI: 10.1038/s43588-026-00978-y

Michael D. Shields, Somdatta Goswami

引用次数: 0

The evolution of gene regulatory networks 基因调控网络的进化

IF 18.3

Nature computational science Pub Date : 2026-04-28 DOI: 10.1038/s43588-026-00981-3

Tatiana Belova, Daniel Osorio, Mariike L. Kuijjer

引用次数: 0

Sustainability as a challenge in complex systems dynamics 可持续性是复杂系统动力学中的一个挑战

IF 18.3

Nature computational science Pub Date : 2026-04-23 DOI: 10.1038/s43588-026-00971-5

Mehrnaz Anvari, Marc Timme

引用次数: 0

Enhancing success rates in therapeutic antibody design through generative models. 通过生成模型提高治疗性抗体设计的成功率。

IF 18.3

Nature computational science Pub Date : 2026-04-17 DOI: 10.1038/s43588-026-00982-2

引用次数: 0

Feature-preserving manifold approximation and projection to analyze single-cell data. 保持特征的流形逼近与投影分析单细胞数据。

IF 18.3

Nature computational science Pub Date : 2026-04-17 DOI: 10.1038/s43588-026-00970-6

Yang Yang, Jialei Gong, Hongjian Sun, Amos Choo, Jessica Cara Mar, Yunbo Wei, Yu Zhang, Wenjie Zhang, Minglei Shu, Zewen Kelvin Tuong, Di Yu

{"title":"Feature-preserving manifold approximation and projection to analyze single-cell data.","authors":"Yang Yang, Jialei Gong, Hongjian Sun, Amos Choo, Jessica Cara Mar, Yunbo Wei, Yu Zhang, Wenjie Zhang, Minglei Shu, Zewen Kelvin Tuong, Di Yu","doi":"10.1038/s43588-026-00970-6","DOIUrl":"https://doi.org/10.1038/s43588-026-00970-6","url":null,"abstract":"Visualizing single-cell data supports understanding cellular heterogeneity and dynamics. Uniform manifold approximation and projection (UMAP) and t-distributed stochastic neighbor embedding (t-SNE) reveal clustering structures but often fail to preserve underlying gene-level information. Here we introduce FeatureMAP (feature-preserving manifold approximation and projection), a framework that enhances manifold learning through pairwise tangent space embedding. By integrating UMAP with principal component analysis, FeatureMAP retains clustering structures and feature variation in a low-dimensional representation. It presents three key analytic concepts: gene contribution, gene variation trajectory, and core versus transition states. Gene contribution and gene variation trajectory are derived by estimating and projecting feature loadings or variation, whereas core and transition states are computationally defined using FeatureMAP's topological properties, including density, curvature and betweenness centrality. These concepts enable differential gene variation(DGV) analysis that highlights regulatory genes driving transitions between cell states. Demonstrated on synthetic and real single-cell RNA sequencing data from pancreatic development and T cell exhaustion, FeatureMAP improves analyses of trajectories and crucial regulatory genes.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147719037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DualGPT-AB: a dual-stage generative optimization framework for therapeutic antibody design. DualGPT-AB：治疗性抗体设计的双阶段生成优化框架。

IF 18.3

Nature computational science Pub Date : 2026-04-15 DOI: 10.1038/s43588-026-00976-0

Dongna Xie, Siyuan Chen, Xi Zeng, Dazhi Lu, Shaoqing Jiao, Shuyuan Xiao, Jiaming Liu, Jianye Hao, Hui Dai, Jiajie Peng

{"title":"DualGPT-AB: a dual-stage generative optimization framework for therapeutic antibody design.","authors":"Dongna Xie, Siyuan Chen, Xi Zeng, Dazhi Lu, Shaoqing Jiao, Shuyuan Xiao, Jiaming Liu, Jianye Hao, Hui Dai, Jiajie Peng","doi":"10.1038/s43588-026-00976-0","DOIUrl":"10.1038/s43588-026-00976-0","url":null,"abstract":"Realizing the therapeutic potential of antibodies requires simultaneously optimizing multiple properties, such as antigen-binding specificity, viscosity, clearance and immunogenicity. However, existing methods used for this task are time consuming and resource intensive, often struggling to balance these properties. Here we propose DualGPT-AB, a dual-stage conditional generative pre-trained transformer (GPT) framework for therapeutic antibody design. DualGPT-AB leverages a conditional GPT to model sequence-property relationships by representing the desired properties as learnable embeddings, while incorporating a reinforcement learning strategy to promote antibody sequence exploration and improve optimization efficiency. Computational experiments show that DualGPT-AB is capable of generating antibody heavy chain complementarity-determining region 3 (CDRH3) sequences fulfilling multiple desired properties. Notably, 8 out of 100 randomly selected antibodies from our designed candidate library exhibit excellent HER2-binding affinities. Wet-laboratory validation confirms that DualGPT-AB identifies antibodies with enhanced tumoricidal activity compared with Herceptin, a widely used antibody drug for treating HER2-positive cancers. Overall, DualGPT-AB is a promising approach for advancing artificial intelligence-driven therapeutic antibody development.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0