Pub Date : 2024-07-30DOI: 10.1038/s43588-024-00664-x
De-en Jiang
Since the first isolation of oxygen, chemists have explored oxygen reduction and evolution reactions. Now, computational chemists are trying to understand and predict the best catalysts for them. Here, the importance of various considerations for such calculations, as well as their challenges and opportunities, are discussed.
{"title":"Computational electrochemistry of oxygen 250 years after Priestley","authors":"De-en Jiang","doi":"10.1038/s43588-024-00664-x","DOIUrl":"10.1038/s43588-024-00664-x","url":null,"abstract":"Since the first isolation of oxygen, chemists have explored oxygen reduction and evolution reactions. Now, computational chemists are trying to understand and predict the best catalysts for them. Here, the importance of various considerations for such calculations, as well as their challenges and opportunities, are discussed.","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":"141857319","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 : 2024-07-24DOI: 10.1038/s43588-024-00659-8
Christian Gaser, Polona Kalc, James H Cole
Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.
{"title":"A perspective on brain-age estimation and its clinical promise.","authors":"Christian Gaser, Polona Kalc, James H Cole","doi":"10.1038/s43588-024-00659-8","DOIUrl":"10.1038/s43588-024-00659-8","url":null,"abstract":"<p><p>Brain-age estimation has gained increased attention in the neuroscientific community owing to its potential use as a biomarker of brain health. The difference between estimated and chronological age based on neuroimaging data enables a unique perspective on brain development and aging, with multiple open questions still remaining in the brain-age research field. This Perspective presents an overview of current advancements in the field and envisions the future evolution of the brain-age framework before its potential deployment in hospital settings.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763150","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 : 2024-07-19DOI: 10.1038/s43588-024-00658-9
Jiancheng Yang
To address the challenge of pretraining foundational models with large datasets, a multi-task approach is proposed, thus helping to overcome the data scarcity problem in biomedical imaging.
{"title":"Multi-task learning for medical foundation models","authors":"Jiancheng Yang","doi":"10.1038/s43588-024-00658-9","DOIUrl":"10.1038/s43588-024-00658-9","url":null,"abstract":"To address the challenge of pretraining foundational models with large datasets, a multi-task approach is proposed, thus helping to overcome the data scarcity problem in biomedical imaging.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728401","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 : 2024-07-19DOI: 10.1038/s43588-024-00666-9
Pretraining powerful deep learning models requires large, comprehensive training datasets, which are often unavailable for medical imaging. In response, the universal biomedical pretrained (UMedPT) foundational model was developed based on multiple small and medium-sized datasets. This model reduced the amount of data required to learn new target tasks by at least 50%.
{"title":"A multi-task learning strategy to pretrain models for medical image analysis","authors":"","doi":"10.1038/s43588-024-00666-9","DOIUrl":"10.1038/s43588-024-00666-9","url":null,"abstract":"Pretraining powerful deep learning models requires large, comprehensive training datasets, which are often unavailable for medical imaging. In response, the universal biomedical pretrained (UMedPT) foundational model was developed based on multiple small and medium-sized datasets. This model reduced the amount of data required to learn new target tasks by at least 50%.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728533","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 : 2024-07-19DOI: 10.1038/s43588-024-00662-z
Raphael Schäfer, Till Nicke, Henning Höfener, Annkristin Lange, Dorit Merhof, Friedrich Feuerhake, Volkmar Schulz, Johannes Lotz, Fabian Kiessling
Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more specialized datasets common in biomedical imaging. Here we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a universal biomedical pretrained model (UMedPT) on a multi-task database including tomographic, microscopic and X-ray images, with various labeling strategies such as classification, segmentation and object detection. The UMedPT foundational model outperformed ImageNet pretraining and previous state-of-the-art models. For classification tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required only 50% of the original training data. In an external independent validation, imaging features extracted using UMedPT proved to set a new standard for cross-center transferability. UMedPT, a foundational model for biomedical imaging, has been trained on a variety of medical tasks with different types of label. It has achieved high performance with less training data in various clinical applications.
经过大规模预训练的基础模型在非医疗领域取得了巨大成功。然而，训练这些模型通常需要大型、全面的数据集，这与生物医学成像中常见的更小、更专业的数据集形成了鲜明对比。在这里，我们提出了一种多任务学习策略，将训练任务的数量与内存要求分离开来。我们在一个多任务数据库上训练了一个通用生物医学预训练模型（UMedPT），该数据库包括断层扫描、显微镜和 X 射线图像，并采用了分类、分割和对象检测等多种标记策略。UMedPT 基础模型的表现优于 ImageNet 预训练模型和以前的先进模型。对于与预训练数据库相关的分类任务，只需使用 1%的原始训练数据，无需微调即可保持性能。对于域外任务，它只需要原始训练数据的 50%。在外部独立验证中，使用 UMedPT 提取的成像特征被证明是跨中心可转移性的新标准。
{"title":"Overcoming data scarcity in biomedical imaging with a foundational multi-task model","authors":"Raphael Schäfer, Till Nicke, Henning Höfener, Annkristin Lange, Dorit Merhof, Friedrich Feuerhake, Volkmar Schulz, Johannes Lotz, Fabian Kiessling","doi":"10.1038/s43588-024-00662-z","DOIUrl":"10.1038/s43588-024-00662-z","url":null,"abstract":"Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more specialized datasets common in biomedical imaging. Here we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a universal biomedical pretrained model (UMedPT) on a multi-task database including tomographic, microscopic and X-ray images, with various labeling strategies such as classification, segmentation and object detection. The UMedPT foundational model outperformed ImageNet pretraining and previous state-of-the-art models. For classification tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required only 50% of the original training data. In an external independent validation, imaging features extracted using UMedPT proved to set a new standard for cross-center transferability. UMedPT, a foundational model for biomedical imaging, has been trained on a variety of medical tasks with different types of label. It has achieved high performance with less training data in various clinical applications.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11288886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728402","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 : 2024-07-18DOI: 10.1038/s43588-024-00663-y
Juan Manuel Restrepo-Flórez
A recent study proposes a computational method for the design of free-form metamaterials systems. The method simplifies the design process by avoiding the use of anisotropic materials that are usually required for the conventional methods. The method can be applied in designing both two-dimensional and three-dimensional metamaterials that are subject to multiple physical fields.
{"title":"Free-form metamaterials design with isotropic materials","authors":"Juan Manuel Restrepo-Flórez","doi":"10.1038/s43588-024-00663-y","DOIUrl":"10.1038/s43588-024-00663-y","url":null,"abstract":"A recent study proposes a computational method for the design of free-form metamaterials systems. The method simplifies the design process by avoiding the use of anisotropic materials that are usually required for the conventional methods. The method can be applied in designing both two-dimensional and three-dimensional metamaterials that are subject to multiple physical fields.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725499","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 : 2024-07-16DOI: 10.1038/s43588-024-00665-w
A graph neural network using virtual nodes is proposed to predict the properties of complex materials with variable dimensions or dimensions that depend on the input. The method is used to accurately and quickly predict phonon dispersion relations in complex solids and alloys.
{"title":"Boosting graph neural networks with virtual nodes to predict phonon properties","authors":"","doi":"10.1038/s43588-024-00665-w","DOIUrl":"10.1038/s43588-024-00665-w","url":null,"abstract":"A graph neural network using virtual nodes is proposed to predict the properties of complex materials with variable dimensions or dimensions that depend on the input. The method is used to accurately and quickly predict phonon dispersion relations in complex solids and alloys.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629477","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 : 2024-07-12DOI: 10.1038/s43588-024-00661-0
Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng, Mingda Li
Understanding the structure–property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility. In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.
{"title":"Virtual node graph neural network for full phonon prediction","authors":"Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Artittaya Boonkird, Nina Andrejevic, Xiang Fu, Tommi S. Jaakkola, Qichen Song, Thanh Nguyen, Nathan Drucker, Sai Mu, Yao Wang, Bolin Liao, Yongqiang Cheng, Mingda Li","doi":"10.1038/s43588-024-00661-0","DOIUrl":"10.1038/s43588-024-00661-0","url":null,"abstract":"Understanding the structure–property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility. In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602338","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 : 2024-07-10DOI: 10.1038/s43588-024-00653-0
Vadim K. Karnaukhov, Dmitrii S. Shcherbinin, Anton O. Chugunov, Dmitriy M. Chudakov, Roman G. Efremov, Ivan V. Zvyagin, Mikhail Shugay
T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR–peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR–peptide–major histocompatibility complex structure. Then a TCR–peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR–peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes. TCRen predicts TCR specificity by modeling the TCR–peptide–MHC structure and estimating the TCR–peptide interaction energy using a statistical potential. The use of structural information allows TCRen to generalize to unseen epitopes, such as cancer neoepitopes.
{"title":"Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen","authors":"Vadim K. Karnaukhov, Dmitrii S. Shcherbinin, Anton O. Chugunov, Dmitriy M. Chudakov, Roman G. Efremov, Ivan V. Zvyagin, Mikhail Shugay","doi":"10.1038/s43588-024-00653-0","DOIUrl":"10.1038/s43588-024-00653-0","url":null,"abstract":"T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR–peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR–peptide–major histocompatibility complex structure. Then a TCR–peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR–peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes. TCRen predicts TCR specificity by modeling the TCR–peptide–MHC structure and estimating the TCR–peptide interaction energy using a statistical potential. The use of structural information allows TCRen to generalize to unseen epitopes, such as cancer neoepitopes.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567219","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 : 2024-07-10DOI: 10.1038/s43588-024-00654-z
Miaozhe Huo, Yuepeng Jiang, Shuai Cheng Li
A method leverages protein structural data to predict T-cell receptor–peptide interactions for unseen peptide epitopes, which can be particularly useful for applications in cancer immunotherapy, autoimmunity studies, and vaccine design.
有一种方法利用蛋白质结构数据来预测未见肽表位的 T 细胞受体与肽的相互作用，这对癌症免疫疗法、自身免疫研究和疫苗设计中的应用特别有用。
{"title":"Unlocking T-cell receptor–epitope insights with structural analysis","authors":"Miaozhe Huo, Yuepeng Jiang, Shuai Cheng Li","doi":"10.1038/s43588-024-00654-z","DOIUrl":"10.1038/s43588-024-00654-z","url":null,"abstract":"A method leverages protein structural data to predict T-cell receptor–peptide interactions for unseen peptide epitopes, which can be particularly useful for applications in cancer immunotherapy, autoimmunity studies, and vaccine design.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":null,"pages":null},"PeriodicalIF":12.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567352","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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