Nature Machine Intelligence最新文献_第2页

Rigorous integration of single-cell ATAC-seq data using regularized barycentric mapping 严格整合单细胞ATAC-seq数据使用正则化质心映射

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-26 DOI: 10.1038/s42256-025-01099-3

Shuchen Zhu, Heyang Hua, Shengquan Chen

{"title":"Rigorous integration of single-cell ATAC-seq data using regularized barycentric mapping","authors":"Shuchen Zhu, Heyang Hua, Shengquan Chen","doi":"10.1038/s42256-025-01099-3","DOIUrl":"https://doi.org/10.1038/s42256-025-01099-3","url":null,"abstract":"Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) deciphers genome-wide chromatin accessibility, providing profound insights into gene regulation mechanisms. With the rapid advance of sequencing technologies, scATAC-seq data typically encompass numerous samples from various conditions, resulting in complex batch effects, thus necessitating reliable integration tools. While numerous batch integration tools exist for single-cell RNA sequencing data, inherent data characteristic differences limit their effectiveness on scATAC-seq data. Existing integration methods for scATAC-seq data suffer from several fundamental limitations, such as disrupting the biological heterogeneity and focusing solely on low-dimensional correction, which may distort data and hinder downstream analysis. Here we propose Fountain, a deep learning framework for scATAC-seq data integration via rigorous barycentric mapping. Barycentric mapping transforms one data distribution to another in a principled and effective manner through optimal transport. By regularizing barycentric mapping with geometric data information, Fountain achieves accurate batch alignment while preserving biological heterogeneity. Comprehensive experiments across diverse real-world datasets demonstrate the advantages of Fountain over existing methods in batch correction and biological conservation. In addition, the trained Fountain model can integrate data from new batches alongside already integrated data without retraining, enabling continuous online data integration. Moreover, Fountain’s reconstruction strategy generates batch-corrected ATAC profiles, improving the capture of cellular heterogeneity and revealing cell-type-specific implications such as expression enrichment analysis and partitioned heritability analysis.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"71 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900548","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

LLMs as all-in-one tools to easily generate publication-ready citation diversity reports 法学硕士是一个多功能的工具，可以轻松地生成出版就绪的引文多样性报告

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-25 DOI: 10.1038/s42256-025-01101-y

Melissa S. Cantú, Michael R. King

{"title":"LLMs as all-in-one tools to easily generate publication-ready citation diversity reports","authors":"Melissa S. Cantú, Michael R. King","doi":"10.1038/s42256-025-01101-y","DOIUrl":"https://doi.org/10.1038/s42256-025-01101-y","url":null,"abstract":"It has been recognized that women and minority scientific authors are cited less frequently than male and majority authors, a systemic phenomenon that negatively affects the visibility and career success of female and minority scientists and biases the prominent research questions of a field1,2. A citation diversity report (CDR), an optional section immediately preceding the reference section of a manuscript, addresses this by quantifying the demographic distribution of cited authors in manuscripts, enabling analysis and potential revision of the proportion of cited scholars from historically excluded groups as a means to advance diversity and inclusivity in science1,2. Journals can also benefit from authors including CDRs in their papers so that they may track the overall citation diversity of their journal.To provide an accurate basis for analysing citation diversity, academic databases such as ORCID have begun to ask authors to voluntarily self-report their gender, race and ethnicity; however, not all scholars choose to disclose this information. Because such data are not widely available at this time, name-based prediction of demographics such as gender, race and ethnicity has become common practice for CDR preparation3. For example, current CDR analysis tools such as cleanBib (https://github.com/dalejn/cleanBib) automate name-based gender and race/ethnicity prediction. However, the databases that cleanBib queries, Gender API and Ethnicolr, have imperfect accuracies of 96.1% and 83%, respectively (ref. 4; https://ethnicolr.readthedocs.io/ethnicolr.html#evaluation).","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"27 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900551","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

Reusability report: Exploring the transferability of self-supervised learning models from single-cell to spatial transcriptomics 可重用性报告：探索自监督学习模型从单细胞到空间转录组学的可转移性

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-21 DOI: 10.1038/s42256-025-01097-5

Chuangyi Han, Senlin Lin, Zhikang Wang, Yan Cui, Qi Zou, Zhiyuan Yuan

{"title":"Reusability report: Exploring the transferability of self-supervised learning models from single-cell to spatial transcriptomics","authors":"Chuangyi Han, Senlin Lin, Zhikang Wang, Yan Cui, Qi Zou, Zhiyuan Yuan","doi":"10.1038/s42256-025-01097-5","DOIUrl":"https://doi.org/10.1038/s42256-025-01097-5","url":null,"abstract":"Self-supervised learning (SSL) has emerged as a powerful approach for learning meaningful representations from large-scale unlabelled datasets in single-cell genomics. Richter et al. evaluated SSL pretext tasks on modelling single-cell RNA sequencing (scRNA-seq) data, demonstrating the effective use of SSL models. However, the transferability of these pretrained SSL models to the spatial transcriptomics domain remains unexplored. Here we assess the performance of three SSL models (random mask, gene programme mask and Barlow Twins) pretrained on scRNA-seq data with spatial transcriptomics datasets, focusing on cell-type prediction and spatial clustering. Our experiments demonstrate that the SSL model with random mask strategy exhibits the best overall performance among evaluated SSL models. Moreover, the models trained from scratch on spatial transcriptomics data outperform the fine-tuned SSL models on cell-type prediction, highlighting a domain gap between scRNA-seq and spatial transcriptomics data whose underlying causes remain an open question. Through expanded analyses of multiple imputation methods and data degradation scenarios, we demonstrate that gene imputation would degrade SSL model performance on cell-type prediction, an effect that is exacerbated by increasing data sparsity. Finally, integrating zero-shot random mask embeddings into chosen spatial clustering methods significantly enhanced their accuracy. Overall, our findings provide valuable insights into the limitations and potential of transferring SSL models to spatial transcriptomics and offer practical guidance for researchers leveraging pretrained models for spatial transcriptomics data analysis.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"15 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900442","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

Towards responsible geospatial foundation models 建立负责任的地理空间基础模型

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-20 DOI: 10.1038/s42256-025-01106-7

引用次数: 0

Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol 基于电子密度的ED2Mol有效可靠的从头分子设计和优化

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-20 DOI: 10.1038/s42256-025-01095-7

Mingyu Li, Kun Song, Jixiao He, Mingzhu Zhao, Gengshu You, Jie Zhong, Mengxi Zhao, Arong Li, Yu Chen, Guobin Li, Ying Kong, Jiacheng Wei, Zhaofu Wang, Jiamin Zhou, Hongbing Yang, Shichao Ma, Hailong Zhang, Irakoze Loïca Mélita, Weidong Lin, Yuhang Lu, Zhengtian Yu, Xun Lu, Yujun Zhao, Jian Zhang

{"title":"Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol","authors":"Mingyu Li, Kun Song, Jixiao He, Mingzhu Zhao, Gengshu You, Jie Zhong, Mengxi Zhao, Arong Li, Yu Chen, Guobin Li, Ying Kong, Jiacheng Wei, Zhaofu Wang, Jiamin Zhou, Hongbing Yang, Shichao Ma, Hailong Zhang, Irakoze Loïca Mélita, Weidong Lin, Yuhang Lu, Zhengtian Yu, Xun Lu, Yujun Zhao, Jian Zhang","doi":"10.1038/s42256-025-01095-7","DOIUrl":"https://doi.org/10.1038/s42256-025-01095-7","url":null,"abstract":"Generative drug design opens avenues for discovering novel compounds within the vast chemical space rather than conventional screening against limited libraries. However, the practical utility of the generated molecules is frequently constrained, as many designs prioritize a narrow range of pharmacological properties and neglect physical reliability, which hinders the success rate of subsequent wet-laboratory evaluations. Here, to address this, we propose ED2Mol, a deep learning-based approach that leverages fundamental electron density information to improve de novo molecular generation and optimization. The extensive evaluations across multiple benchmarks demonstrate that ED2Mol surpasses existing methods in terms of the generation success rate and >97% physical reliability. It also facilitates automated hit optimization that is not fully implemented by other methods using fragment-based strategies. Furthermore, ED2Mol exhibits generalizability to more challenging, unseen allosteric pocket benchmarks, attaining consistent performance. More importantly, ED2Mol has been applied to various real-world essential targets, successfully identifying wet-laboratory-validated bioactive compounds, ranging from FGFR3 orthosteric inhibitors to CDC42 allosteric inhibitors, GCK and GPRC5A allosteric activators. The directly generated binding modes of these compounds are close to predictions through molecular docking and further validated via the X-ray co-crystal structure. All these results highlight ED2Mol’s potential as a useful tool in drug design with enhanced effectiveness, physical reliability and practical applicability.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"23 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144901527","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

Training data composition determines machine learning generalization and biological rule discovery 训练数据的组成决定了机器学习的泛化和生物规则的发现

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-20 DOI: 10.1038/s42256-025-01089-5

Eugen Ursu, Aygul Minnegalieva, Puneet Rawat, Maria Chernigovskaya, Robi Tacutu, Geir Kjetil Sandve, Philippe A. Robert, Victor Greiff

{"title":"Training data composition determines machine learning generalization and biological rule discovery","authors":"Eugen Ursu, Aygul Minnegalieva, Puneet Rawat, Maria Chernigovskaya, Robi Tacutu, Geir Kjetil Sandve, Philippe A. Robert, Victor Greiff","doi":"10.1038/s42256-025-01089-5","DOIUrl":"https://doi.org/10.1038/s42256-025-01089-5","url":null,"abstract":"Supervised machine learning models depend on training datasets containing positive and negative examples: dataset composition directly impacts model performance and bias. Given the importance of machine learning for immunotherapeutic design, we examined how different negative class definitions affect model generalization and rule discovery for antibody–antigen binding. Using synthetic-structure-based binding data, we evaluated models trained with various definitions of negative sets. Our findings reveal that high out-of-distribution performance can be achieved when the negative dataset contains more similar samples to the positive dataset, despite lower in-distribution performance. Furthermore, by leveraging ground-truth information, we show that binding rules associated with positive data change based on the negative data used. Validation on experimental data supported simulation-based observations. This work underscores the role of dataset composition in creating robust, generalizable and biology-aware sequence-based ML models.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"25 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898527","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

The importance of negative training data for robust antibody binding prediction 阴性训练数据对稳健抗体结合预测的重要性

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-20 DOI: 10.1038/s42256-025-01080-0

Wesley Ta, Jonathan M. Stokes

引用次数: 0

A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning 一个统一的预训练深度学习框架，用于跨任务反应性能预测和综合规划

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-19 DOI: 10.1038/s42256-025-01098-4

Li-Cheng Xu, Miao-Jiong Tang, Junyi An, Fenglei Cao, Yuan Qi

{"title":"A unified pre-trained deep learning framework for cross-task reaction performance prediction and synthesis planning","authors":"Li-Cheng Xu, Miao-Jiong Tang, Junyi An, Fenglei Cao, Yuan Qi","doi":"10.1038/s42256-025-01098-4","DOIUrl":"https://doi.org/10.1038/s42256-025-01098-4","url":null,"abstract":"Artificial intelligence has transformed the field of precise organic synthesis. Data-driven methods, including machine learning and deep learning, have shown great promise in predicting reaction performance and synthesis planning. However, the inherent methodological divergence between numerical regression-driven reaction performance prediction and sequence generation-based synthesis planning creates formidable challenges in constructing a unified deep learning architecture. Here we present RXNGraphormer, a framework to jointly address these tasks through a unified pre-training approach. By synergizing graph neural networks for intramolecular pattern recognition with Transformer-based models for intermolecular interaction modelling, and training on 13 million reactions via a carefully designed strategy, RXNGraphormer achieves state-of-the-art performance across eight benchmark datasets for reactivity or selectivity prediction and forward-synthesis or retrosynthesis planning, as well as three external realistic datasets for reactivity and selectivity prediction. Notably, the model generates chemically meaningful embeddings that spontaneously cluster reactions by type without explicit supervision. This work bridges the critical gap between performance prediction and synthesis planning tasks in chemical AI, offering a versatile tool for accurate reaction prediction and synthesis design.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"146 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898568","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

Informed protein–ligand docking via geodesic guidance in translational, rotational and torsional spaces 通过在平移，旋转和扭转空间的测地线引导，了解蛋白质配体对接

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-15 DOI: 10.1038/s42256-025-01091-x

Raúl Miñán, Javier Gallardo, Álvaro Ciudad, Alexis Molina

{"title":"Informed protein–ligand docking via geodesic guidance in translational, rotational and torsional spaces","authors":"Raúl Miñán, Javier Gallardo, Álvaro Ciudad, Alexis Molina","doi":"10.1038/s42256-025-01091-x","DOIUrl":"https://doi.org/10.1038/s42256-025-01091-x","url":null,"abstract":"Molecular docking plays a crucial role in structure-based drug discovery, enabling the prediction of how small molecules interact with protein targets. Traditional docking methods rely on scoring functions and search heuristics, whereas recent generative approaches, such as DiffDock, leverage deep learning for pose prediction. However, blind-diffusion-based docking often struggles with binding site localization and pose accuracy, particularly in complex protein–ligand systems. This work introduces GeoDirDock (GDD), a guided diffusion approach to molecular docking that enhances the accuracy and physical plausibility of ligand docking predictions. GDD guides the denoising process of a diffusion model along geodesic paths within multiple spaces representing translational, rotational and torsional degrees of freedom. Our method leverages expert knowledge to direct the generative modelling process, specifically targeting desired protein–ligand interaction regions. We demonstrate that GDD outperforms existing blind docking methods in terms of root mean squared distance accuracy and physicochemical pose realism. Our results indicate that incorporating domain expertise into the diffusion process leads to more biologically relevant docking predictions. Additionally, we explore the potential of GDD as a template-based modelling tool for lead optimization in drug discovery through angle transfer in maximum common substructure docking, showcasing its capability to accurately predict ligand orientations for chemically similar compounds. Future applications in real-world drug discovery campaigns will naturally continue to refine and extend the utility of prior-informed diffusion docking methods.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851536","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

Mechanistic understanding and validation of large AI models with SemanticLens 基于SemanticLens的大型人工智能模型的机理理解和验证

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2025-08-14 DOI: 10.1038/s42256-025-01084-w

Maximilian Dreyer, Jim Berend, Tobias Labarta, Johanna Vielhaben, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek

{"title":"Mechanistic understanding and validation of large AI models with SemanticLens","authors":"Maximilian Dreyer, Jim Berend, Tobias Labarta, Johanna Vielhaben, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek","doi":"10.1038/s42256-025-01084-w","DOIUrl":"https://doi.org/10.1038/s42256-025-01084-w","url":null,"abstract":"Unlike human-engineered systems, such as aeroplanes, for which the role and dependencies of each component are well understood, the inner workings of artificial intelligence models remain largely opaque, which hinders verifiability and undermines trust. Current approaches to neural network interpretability, including input attribution methods, probe-based analysis and activation visualization techniques, typically provide limited insights about the role of individual components or require extensive manual interpretation that cannot scale with model complexity. This paper introduces SemanticLens, a universal explanation method for neural networks that maps hidden knowledge encoded by components (for example, individual neurons) into the semantically structured, multimodal space of a foundation model such as CLIP. In this space, unique operations become possible, including (1) textual searches to identify neurons encoding specific concepts, (2) systematic analysis and comparison of model representations, (3) automated labelling of neurons and explanation of their functional roles, and (4) audits to validate decision-making against requirements. Fully scalable and operating without human input, SemanticLens is shown to be effective for debugging and validation, summarizing model knowledge, aligning reasoning with expectations (for example, adherence to the ABCDE rule in melanoma classification) and detecting components tied to spurious correlations and their associated training data. By enabling component-level understanding and validation, the proposed approach helps mitigate the opacity that limits confidence in artificial intelligence systems compared to traditional engineered systems, enabling more reliable deployment in critical applications.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"95 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840346","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