Pub Date : 2024-08-22DOI: 10.1038/s43588-024-00682-9
Revealing a drug’s mechanism of action (MOA) is costly and time-consuming. In this study, we used deep learning to extract temporal mitochondrial phenotypic features after exposure to drugs with known MOAs using re-identification algorithms. The trained model could then predict the MOAs of unidentified substances, facilitating phenotypic screening-based drug discovery and repurposing.
{"title":"AI-recognized mitochondrial phenotype enables identification of drug targets","authors":"","doi":"10.1038/s43588-024-00682-9","DOIUrl":"10.1038/s43588-024-00682-9","url":null,"abstract":"Revealing a drug’s mechanism of action (MOA) is costly and time-consuming. In this study, we used deep learning to extract temporal mitochondrial phenotypic features after exposure to drugs with known MOAs using re-identification algorithms. The trained model could then predict the MOAs of unidentified substances, facilitating phenotypic screening-based drug discovery and repurposing.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037931","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}
Pub Date : 2024-08-21DOI: 10.1038/s43588-024-00679-4
Min Yu, Weiming Li, Yunru Yu, Yu Zhao, Lizhi Xiao, Volker M. Lauschke, Yiyu Cheng, Xingcai Zhang, Yi Wang
Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing. A deep learning-based model, MitoReID, is presented for profiling changes in mitochondrial phenotypes, allowing for the identification of various drugs’ mechanism of action.
{"title":"Deep learning large-scale drug discovery and repurposing","authors":"Min Yu, Weiming Li, Yunru Yu, Yu Zhao, Lizhi Xiao, Volker M. Lauschke, Yiyu Cheng, Xingcai Zhang, Yi Wang","doi":"10.1038/s43588-024-00679-4","DOIUrl":"10.1038/s43588-024-00679-4","url":null,"abstract":"Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing. A deep learning-based model, MitoReID, is presented for profiling changes in mitochondrial phenotypes, allowing for the identification of various drugs’ mechanism of action.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019787","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}
Pub Date : 2024-08-16DOI: 10.1038/s43588-024-00677-6
Rui-Jie Zhu, Skye Gunasekaran, Jason Eshraghian
According to a recent study, a small network consisting of four leaky integrate-and-fire neurons can reproduce the behavior of a single Hodgkin–Huxley neuron, thereby bridging the gap between endogenous and exogenous complexity.
{"title":"Bridging the gap between artificial intelligence and natural intelligence","authors":"Rui-Jie Zhu, Skye Gunasekaran, Jason Eshraghian","doi":"10.1038/s43588-024-00677-6","DOIUrl":"10.1038/s43588-024-00677-6","url":null,"abstract":"According to a recent study, a small network consisting of four leaky integrate-and-fire neurons can reproduce the behavior of a single Hodgkin–Huxley neuron, thereby bridging the gap between endogenous and exogenous complexity.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141997046","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}
Pub Date : 2024-08-16DOI: 10.1038/s43588-024-00678-5
Ting Cao
By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.
{"title":"Accelerating predictions of electronic transport and superconductivity","authors":"Ting Cao","doi":"10.1038/s43588-024-00678-5","DOIUrl":"10.1038/s43588-024-00678-5","url":null,"abstract":"By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141997045","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}
Pub Date : 2024-08-16DOI: 10.1038/s43588-024-00674-9
Linxuan He, Yunhui Xu, Weihua He, Yihan Lin, Yang Tian, Yujie Wu, Wenhui Wang, Ziyang Zhang, Junwei Han, Yonghong Tian, Bo Xu, Guoqi Li
Artificial intelligence (AI) researchers currently believe that the main approach to building more general model problems is the big AI model, where existing neural networks are becoming deeper, larger and wider. We term this the big model with external complexity approach. In this work we argue that there is another approach called small model with internal complexity, which can be used to find a suitable path of incorporating rich properties into neurons to construct larger and more efficient AI models. We uncover that one has to increase the scale of the network externally to stimulate the same dynamical properties. To illustrate this, we build a Hodgkin–Huxley (HH) network with rich internal complexity, where each neuron is an HH model, and prove that the dynamical properties and performance of the HH network can be equivalent to a bigger leaky integrate-and-fire (LIF) network, where each neuron is a LIF neuron with simple internal complexity. This study shows that by enhancing internal complexity of neurons in a Hodgkin–Huxley network, similar performance to larger, simpler networks can be achieved, suggesting an alternative path for powerful AI systems by focusing on neuron complexity.
{"title":"Network model with internal complexity bridges artificial intelligence and neuroscience","authors":"Linxuan He, Yunhui Xu, Weihua He, Yihan Lin, Yang Tian, Yujie Wu, Wenhui Wang, Ziyang Zhang, Junwei Han, Yonghong Tian, Bo Xu, Guoqi Li","doi":"10.1038/s43588-024-00674-9","DOIUrl":"10.1038/s43588-024-00674-9","url":null,"abstract":"Artificial intelligence (AI) researchers currently believe that the main approach to building more general model problems is the big AI model, where existing neural networks are becoming deeper, larger and wider. We term this the big model with external complexity approach. In this work we argue that there is another approach called small model with internal complexity, which can be used to find a suitable path of incorporating rich properties into neurons to construct larger and more efficient AI models. We uncover that one has to increase the scale of the network externally to stimulate the same dynamical properties. To illustrate this, we build a Hodgkin–Huxley (HH) network with rich internal complexity, where each neuron is an HH model, and prove that the dynamical properties and performance of the HH network can be equivalent to a bigger leaky integrate-and-fire (LIF) network, where each neuron is a LIF neuron with simple internal complexity. This study shows that by enhancing internal complexity of neurons in a Hodgkin–Huxley network, similar performance to larger, simpler networks can be achieved, suggesting an alternative path for powerful AI systems by focusing on neuron complexity.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141997048","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}
The calculation of electron–phonon couplings (EPCs) is essential for understanding various fundamental physical properties, including electrical transport, optical and superconducting behaviors in materials. However, obtaining EPCs through fully first-principles methods is notably challenging, particularly for large systems or when employing advanced functionals. Here we introduce a machine learning framework to accelerate EPC calculations by utilizing atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network. We demonstrate that our method not only yields EPC values in close agreement with first-principles results but also enhances calculation efficiency by several orders of magnitude. Application to GaAs using the Heyd–Scuseria–Ernzerhof functional reveals the necessity of advanced functionals for accurate carrier mobility predictions, while for the large Kagome crystal CsV3Sb5, our framework reproduces the experimentally observed double domes in pressure-induced superconducting phase diagrams. This machine learning framework offers a powerful and efficient tool for the investigation of diverse EPC-related phenomena in complex materials. A machine learning framework is proposed to accurately predict electron–phonon coupling (EPC) strengths while reducing computational costs compared with first-principles methods. This approach facilitates EPC calculations with advanced functionals, allowing the accurate determination of real-world material properties such as carrier mobility and superconductivity.
{"title":"Accelerating the calculation of electron–phonon coupling strength with machine learning","authors":"Yang Zhong, Shixu Liu, Binhua Zhang, Zhiguo Tao, Yuting Sun, Weibin Chu, Xin-Gao Gong, Ji-Hui Yang, Hongjun Xiang","doi":"10.1038/s43588-024-00668-7","DOIUrl":"10.1038/s43588-024-00668-7","url":null,"abstract":"The calculation of electron–phonon couplings (EPCs) is essential for understanding various fundamental physical properties, including electrical transport, optical and superconducting behaviors in materials. However, obtaining EPCs through fully first-principles methods is notably challenging, particularly for large systems or when employing advanced functionals. Here we introduce a machine learning framework to accelerate EPC calculations by utilizing atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network. We demonstrate that our method not only yields EPC values in close agreement with first-principles results but also enhances calculation efficiency by several orders of magnitude. Application to GaAs using the Heyd–Scuseria–Ernzerhof functional reveals the necessity of advanced functionals for accurate carrier mobility predictions, while for the large Kagome crystal CsV3Sb5, our framework reproduces the experimentally observed double domes in pressure-induced superconducting phase diagrams. This machine learning framework offers a powerful and efficient tool for the investigation of diverse EPC-related phenomena in complex materials. A machine learning framework is proposed to accurately predict electron–phonon coupling (EPC) strengths while reducing computational costs compared with first-principles methods. This approach facilitates EPC calculations with advanced functionals, allowing the accurate determination of real-world material properties such as carrier mobility and superconductivity.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908581","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}
Pub Date : 2024-08-08DOI: 10.1038/s43588-024-00680-x
A machine learning framework that uses atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network is established to calculate electron–phonon coupling (EPC). This approach accelerates the calculations by several orders of magnitude, enabling EPC-related properties to be predicted for complex systems using highly accurate functionals.
{"title":"A machine learning tool to efficiently calculate electron–phonon coupling","authors":"","doi":"10.1038/s43588-024-00680-x","DOIUrl":"10.1038/s43588-024-00680-x","url":null,"abstract":"A machine learning framework that uses atomic orbital-based Hamiltonian matrices and gradients predicted by an equivariant graph neural network is established to calculate electron–phonon coupling (EPC). This approach accelerates the calculations by several orders of magnitude, enabling EPC-related properties to be predicted for complex systems using highly accurate functionals.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908856","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}
Pub Date : 2024-07-30DOI: 10.1038/s43588-024-00670-z
We look back on the discovery of oxygen in light of its upcoming milestone anniversary and highlight the computational contributions to oxygen reduction and evolution in chemistry.
{"title":"250 years of oxygen chemistry","authors":"","doi":"10.1038/s43588-024-00670-z","DOIUrl":"10.1038/s43588-024-00670-z","url":null,"abstract":"We look back on the discovery of oxygen in light of its upcoming milestone anniversary and highlight the computational contributions to oxygen reduction and evolution in chemistry.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00670-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857318","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}
Pub Date : 2024-07-30DOI: 10.1038/s43588-024-00681-w
Christian Gaser, Polona Kalc, James H Cole
{"title":"Publisher Correction: A perspective on brain-age estimation and its clinical promise.","authors":"Christian Gaser, Polona Kalc, James H Cole","doi":"10.1038/s43588-024-00681-w","DOIUrl":"10.1038/s43588-024-00681-w","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857317","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}
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