Nature computational science最新文献_第2页

Effective quantum error correction by AI

IF 12

Nature computational science Pub Date : 2024-12-11 DOI: 10.1038/s43588-024-00755-9

Jie Pan

引用次数: 0

A simulated annealing algorithm for randomizing weighted networks.

IF 12

Nature computational science Pub Date : 2024-12-10 DOI: 10.1038/s43588-024-00735-z

Filip Milisav, Vincent Bazinet, Richard F Betzel, Bratislav Misic

{"title":"A simulated annealing algorithm for randomizing weighted networks.","authors":"Filip Milisav, Vincent Bazinet, Richard F Betzel, Bratislav Misic","doi":"10.1038/s43588-024-00735-z","DOIUrl":"https://doi.org/10.1038/s43588-024-00735-z","url":null,"abstract":"<p><p>Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807370","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

A scalable framework for learning the geometry-dependent solution operators of partial differential equations

IF 12

Nature computational science Pub Date : 2024-12-09 DOI: 10.1038/s43588-024-00732-2

Minglang Yin, Nicolas Charon, Ryan Brody, Lu Lu, Natalia Trayanova, Mauro Maggioni

{"title":"A scalable framework for learning the geometry-dependent solution operators of partial differential equations","authors":"Minglang Yin, Nicolas Charon, Ryan Brody, Lu Lu, Natalia Trayanova, Mauro Maggioni","doi":"10.1038/s43588-024-00732-2","DOIUrl":"10.1038/s43588-024-00732-2","url":null,"abstract":"Solving partial differential equations (PDEs) using numerical methods is a ubiquitous task in engineering and medicine. However, the computational costs can be prohibitively high when many-query evaluations of PDE solutions on multiple geometries are needed. Here we aim to address this challenge by introducing Diffeomorphic Mapping Operator Learning (DIMON), a generic artificial intelligence framework that learns geometry-dependent solution operators of different types of PDE on a variety of geometries. We present several examples to demonstrate the performance, efficiency and scalability of the framework in learning both static and time-dependent PDEs on parameterized and non-parameterized domains; these include solving the Laplace equations, reaction–diffusion equations and a system of multiscale PDEs that characterize the electrical propagation on thousands of personalized heart digital twins. DIMON can reduce the computational costs of solution approximations on multiple geometries from hours to seconds with substantially less computational resources. This work presents an artificial intelligence framework to learn geometry-dependent solution operators of partial differential equations (PDEs). The framework enables scalable and fast approximations of PDE solutions on a variety of 3D geometries.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"928-940"},"PeriodicalIF":12.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00732-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803066","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

Structure-based drug design with equivariant diffusion models

IF 12

Nature computational science Pub Date : 2024-12-09 DOI: 10.1038/s43588-024-00737-x

Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom L. Blundell, Pietro Lio, Max Welling, Michael Bronstein, Bruno Correia

{"title":"Structure-based drug design with equivariant diffusion models","authors":"Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom L. Blundell, Pietro Lio, Max Welling, Michael Bronstein, Bruno Correia","doi":"10.1038/s43588-024-00737-x","DOIUrl":"10.1038/s43588-024-00737-x","url":null,"abstract":"Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods leverage structural data of drugs with their protein targets to propose new drug candidates. However, most existing methods focus exclusively on bottom-up de novo design of compounds or tackle other drug development challenges with task-specific models. The latter requires curation of suitable datasets, careful engineering of the models and retraining from scratch for each task. Here we show how a single pretrained diffusion model can be applied to a broader range of problems, such as off-the-shelf property optimization, explicit negative design and partial molecular design with inpainting. We formulate SBDD as a three-dimensional conditional generation problem and present DiffSBDD, an SE(3)-equivariant diffusion model that generates novel ligands conditioned on protein pockets. Furthermore, we show how additional constraints can be used to improve the generated drug candidates according to a variety of computational metrics. This work applies diffusion models to conditional molecule generation and shows how they can be used to tackle various structure-based drug design problems","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"899-909"},"PeriodicalIF":12.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00737-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803183","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

Enabling efficient analysis of biobank-scale data with genotype representation graphs.

IF 12

Nature computational science Pub Date : 2024-12-05 DOI: 10.1038/s43588-024-00739-9

Drew DeHaas, Ziqing Pan, Xinzhu Wei

引用次数: 0

Teaching spin symmetry while learning neural network wave functions

IF 12

Nature computational science Pub Date : 2024-12-04 DOI: 10.1038/s43588-024-00727-z

Yongle Li, Yuhao Chen, Xiao He

引用次数: 0

Deep learning training dynamics analysis for single-cell data

IF 12

Nature computational science Pub Date : 2024-12-04 DOI: 10.1038/s43588-024-00728-y

引用次数: 0

Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network

IF 12

Nature computational science Pub Date : 2024-12-04 DOI: 10.1038/s43588-024-00730-4

Zhe Li, Zixiang Lu, Ruichen Li, Xuelan Wen, Xiang Li, Liwei Wang, Ji Chen, Weiluo Ren

{"title":"Spin-symmetry-enforced solution of the many-body Schrödinger equation with a deep neural network","authors":"Zhe Li, Zixiang Lu, Ruichen Li, Xuelan Wen, Xiang Li, Liwei Wang, Ji Chen, Weiluo Ren","doi":"10.1038/s43588-024-00730-4","DOIUrl":"10.1038/s43588-024-00730-4","url":null,"abstract":"The integration of deep neural networks with the variational Monte Carlo (VMC) method has marked a substantial advancement in solving the Schrödinger equation. In this work we enforce spin symmetry in the neural-network-based VMC calculation using a modified optimization target. Our method is designed to solve for the ground state and multiple excited states with target spin symmetry at a low computational cost. It predicts accurate energies while maintaining the correct symmetry in strongly correlated systems, even in cases in which different spin states are nearly degenerate. Our approach also excels at spin–gap calculations, including the singlet–triplet gap in biradical systems, which is of high interest in photochemistry. Overall, this work establishes a robust framework for efficiently calculating various quantum states with specific spin symmetry in correlated systems. An efficient approach is developed to enforce spin symmetry for neural network wavefunctions when solving the many-body Schrödinger equation. This enables accurate and spin-pure simulations of both ground and excited states.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"910-919"},"PeriodicalIF":12.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782028","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

Interpreting single-cell and spatial omics data using deep neural network training dynamics

IF 12

Nature computational science Pub Date : 2024-12-04 DOI: 10.1038/s43588-024-00721-5

Jonathan Karin, Reshef Mintz, Barak Raveh, Mor Nitzan

{"title":"Interpreting single-cell and spatial omics data using deep neural network training dynamics","authors":"Jonathan Karin, Reshef Mintz, Barak Raveh, Mor Nitzan","doi":"10.1038/s43588-024-00721-5","DOIUrl":"10.1038/s43588-024-00721-5","url":null,"abstract":"Single-cell and spatial omics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations or phenotypes. However, cell annotations are inherently ambiguous, as discrete labels with subjective interpretations are assigned to heterogeneous cell populations on the basis of noisy, sparse and high-dimensional data. Here we developed Annotatability, a framework for identifying annotation mismatches and characterizing biological data structure by monitoring the dynamics and difficulty of training a deep neural network over such annotated data. Following this, we developed a signal-aware graph embedding method that enables downstream analysis of biological signals. This embedding captures cellular communities associated with target signals. Using Annotatability, we address key challenges in the interpretation of genomic data, demonstrated over eight single-cell RNA sequencing and spatial omics datasets, including identifying erroneous annotations and intermediate cell states, delineating developmental or disease trajectories, and capturing cellular heterogeneity. These results underscore the broad applicability of annotation-trainability analysis via Annotatability for unraveling cellular diversity and interpreting collective cell behaviors in health and disease. The Annotatability framework analyzes neural network training dynamics to interpret single-cell and spatial omics data. It identifies erroneous annotations and ambiguous cell states, infers trajectories from binary labels and enables signal-aware analysis.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"941-954"},"PeriodicalIF":12.0,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00721-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782027","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

Comprehensive prediction and analysis of human protein essentiality based on a pretrained large language model. 基于预训练大型语言模型的人类蛋白质本质综合预测与分析。

IF 12

Nature computational science Pub Date : 2024-11-27 DOI: 10.1038/s43588-024-00733-1

Boming Kang, Rui Fan, Chunmei Cui, Qinghua Cui

引用次数: 0