Nature computational science最新文献_第10页

A multidimensional dataset for structure-based machine learning 基于结构的机器学习多维数据集。

Nature computational science Pub Date : 2024-05-14 DOI: 10.1038/s43588-024-00631-6

Matthew Holcomb, Stefano Forli

引用次数: 0

MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery MISATO：基于结构发现药物的蛋白质配体机器学习数据集。

Nature computational science Pub Date : 2024-05-10 DOI: 10.1038/s43588-024-00627-2

Till Siebenmorgen, Filipe Menezes, Sabrina Benassou, Erinc Merdivan, Kieran Didi, André Santos Dias Mourão, Radosław Kitel, Pietro Liò, Stefan Kesselheim, Marie Piraud, Fabian J. Theis, Michael Sattler, Grzegorz M. Popowicz

{"title":"MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery","authors":"Till Siebenmorgen, Filipe Menezes, Sabrina Benassou, Erinc Merdivan, Kieran Didi, André Santos Dias Mourão, Radosław Kitel, Pietro Liò, Stefan Kesselheim, Marie Piraud, Fabian J. Theis, Michael Sattler, Grzegorz M. Popowicz","doi":"10.1038/s43588-024-00627-2","DOIUrl":"10.1038/s43588-024-00627-2","url":null,"abstract":"Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule–ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein–ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein–ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models. MISATO is a database for structure-based drug discovery that combines quantum mechanics data with molecular dynamics simulations on ~20,000 protein–ligand structures. The artificial intelligence models included provide an easy entry point for the machine learning and drug discovery communities.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00627-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity 用于揭示细胞异质性的单细胞染色质可及性测序数据的离散潜在嵌入。

Nature computational science Pub Date : 2024-05-10 DOI: 10.1038/s43588-024-00625-4

Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen, Rui Jiang

{"title":"Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity","authors":"Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen, Rui Jiang","doi":"10.1038/s43588-024-00625-4","DOIUrl":"10.1038/s43588-024-00625-4","url":null,"abstract":"Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models—especially variational autoencoders—have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE’s capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively. A method based on a vector-quantized variational autoencoder, called CASTLE, can interpretably extract discrete latent embeddings and quantitatively generate the cell-type-specific feature spectrum for single-cell chromatin accessibility sequencing data.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140905261","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

Publisher Correction: SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization 出版商更正：SimuCell3D：带细胞极化的组织力学三维模拟。

Nature computational science Pub Date : 2024-05-06 DOI: 10.1038/s43588-024-00635-2

Steve Runser, Roman Vetter, Dagmar Iber

引用次数: 0

Advancements in multicellular simulations 多细胞模拟的进展

Nature computational science Pub Date : 2024-05-02 DOI: 10.1038/s43588-024-00624-5

Domenic P. J. Germano, James M. Osborne

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Harnessing quantum information to advance computing 利用量子信息推动计算发展

Nature computational science Pub Date : 2024-04-26 DOI: 10.1038/s43588-024-00628-1

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In search of the most cooperative network 寻找最具合作性的网络

Nature computational science Pub Date : 2024-04-25 DOI: 10.1038/s43588-024-00623-6

Valerio Capraro, Matjaž Perc

引用次数: 0

Annotating cell types in single-cell ATAC data via the guidance of the underlying DNA sequences 通过底层 DNA 序列的指导来标注单细胞 ATAC 数据中的细胞类型

Nature computational science Pub Date : 2024-04-22 DOI: 10.1038/s43588-024-00626-3

引用次数: 0

Discovering metal complexes in vast chemical spaces 在广阔的化学空间中发现金属复合物

Nature computational science Pub Date : 2024-04-18 DOI: 10.1038/s43588-024-00618-3

引用次数: 0

Strategy evolution on higher-order networks 高阶网络上的战略演变

Nature computational science Pub Date : 2024-04-15 DOI: 10.1038/s43588-024-00621-8

Anzhi Sheng, Qi Su, Long Wang, Joshua B. Plotkin

{"title":"Strategy evolution on higher-order networks","authors":"Anzhi Sheng, Qi Su, Long Wang, Joshua B. Plotkin","doi":"10.1038/s43588-024-00621-8","DOIUrl":"10.1038/s43588-024-00621-8","url":null,"abstract":"Cooperation is key to prosperity in human societies. Population structure is well understood as a catalyst for cooperation, where research has focused on pairwise interactions. But cooperative behaviors are not simply dyadic, and they often involve coordinated behavior in larger groups. Here we develop a framework to study the evolution of behavioral strategies in higher-order population structures, which include pairwise and multi-way interactions. We provide an analytical treatment of when cooperation will be favored by higher-order interactions, accounting for arbitrary spatial heterogeneity and nonlinear rewards for cooperation in larger groups. Our results indicate that higher-order interactions can act to promote the evolution of cooperation across a broad range of networks, in public goods games. Higher-order interactions consistently provide an advantage for cooperation when interaction hyper-networks feature multiple conjoined communities. Our analysis provides a systematic account of how higher-order interactions modulate the evolution of prosocial traits. Cooperation is not merely a dyadic phenomenon, it also includes multi-way social interactions. A mathematical framework is developed to study how the structure of higher-order interactions influences cooperative behavior.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140580608","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