Nature Machine Intelligence最新文献

{"title":"Reusability report: exploring the utility of variational graph encoders for predicting molecular toxicity in drug design","authors":"Ruijiang Li, Jiang Lu, Ziyi Liu, Duoyun Yi, Mengxuan Wan, Yixin Zhang, Peng Zan, Song He, Xiaochen Bo","doi":"10.1038/s42256-024-00923-6","DOIUrl":"10.1038/s42256-024-00923-6","url":null,"abstract":"Variational graph encoders effectively combine graph convolutional networks with variational autoencoders, and have been widely employed for biomedical graph-structured data. Lam and colleagues developed a framework based on the variational graph encoder, NYAN, to facilitate the prediction of molecular properties in computer-assisted drug design. In NYAN, the low-dimensional latent variables derived from the variational graph autoencoder are leveraged as a universal molecular representation, yielding remarkable performance and versatility throughout the drug discovery process. In this study we assess the reusability of NYAN and investigate its applicability within the context of specific chemical toxicity prediction. The prediction accuracy—based on NYAN latent representations and other popular molecular feature representations—is benchmarked across a broad spectrum of toxicity datasets, and the adaptation of NYAN latent representation to other surrogate models is also explored. NYAN, equipped with common surrogate models, shows competitive or better performance in toxicity prediction compared with other state-of-the-art molecular property prediction methods. We also devise a multi-task learning strategy with feature enhancement and consensus inference by leveraging the low dimensionality and feature diversity of NYAN latent space, further boosting the multi-endpoint acute toxicity estimation. The analysis delves into the adaptability of the generic graph variational model, showcasing its aptitude for tailored tasks within the realm of drug discovery. Ruijiang Li et al. assess the reproducibility of a variational graph encoder-based framework and examines its reusability for chemical toxicity prediction. It explores how a generalist model can function as a specialist model with adaptation.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 12","pages":"1457-1466"},"PeriodicalIF":18.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00923-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142596493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"General-purpose foundation models for increased autonomy in robot-assisted surgery","authors":"Samuel Schmidgall, Ji Woong Kim, Alan Kuntz, Ahmed Ezzat Ghazi, Axel Krieger","doi":"10.1038/s42256-024-00917-4","DOIUrl":"10.1038/s42256-024-00917-4","url":null,"abstract":"The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific objectives that solve a single robotic problem such as picking up an object or reaching a target position. However, recent work on high-capacity models in robotics has shown promise towards being trained on large collections of diverse and task-agnostic datasets of video demonstrations. These models have shown impressive levels of generalization to unseen circumstances, especially as the amount of data and the model complexity scale. Surgical robot systems that learn from data have struggled to advance as quickly as other fields of robot learning for a few reasons: there is a lack of existing large-scale open-source data to train models; it is challenging to model the soft-body deformations that these robots work with during surgery because simulation cannot match the physical and visual complexity of biological tissue; and surgical robots risk harming patients when tested in clinical trials and require more extensive safety measures. This Perspective aims to provide a path towards increasing robot autonomy in robot-assisted surgery through the development of a multi-modal, multi-task, vision–language–action model for surgical robots. Ultimately, we argue that surgical robots are uniquely positioned to benefit from general-purpose models and provide four guiding actions towards increased autonomy in robot-assisted surgery. Schmidgall et al. describe a pathway for building general-purpose machine learning models for robot-assisted surgery, including mechanisms for avoiding risk and handing over control to surgeons, and improving safety and outcomes beyond demonstration data.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 11","pages":"1275-1283"},"PeriodicalIF":18.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142562164","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}

{"title":"Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging","authors":"Sergios Gatidis, Marcel Früh, Matthias P. Fabritius, Sijing Gu, Konstantin Nikolaou, Christian La Fougère, Jin Ye, Junjun He, Yige Peng, Lei Bi, Jun Ma, Bo Wang, Jia Zhang, Yukun Huang, Lars Heiliger, Zdravko Marinov, Rainer Stiefelhagen, Jan Egger, Jens Kleesiek, Ludovic Sibille, Lei Xiang, Simone Bendazzoli, Mehdi Astaraki, Michael Ingrisch, Clemens C. Cyran, Thomas Küstner","doi":"10.1038/s42256-024-00912-9","DOIUrl":"10.1038/s42256-024-00912-9","url":null,"abstract":"Automated detection of tumour lesions on positron emission tomography–computed tomography (PET/CT) image data is a clinically relevant but highly challenging task. Progress in this field has been hampered in the past owing to the lack of publicly available annotated data and limited availability of platforms for inter-institutional collaboration. Here we describe the results of the autoPET challenge, a biomedical image analysis challenge aimed to motivate research in the field of automated PET/CT image analysis. The challenge task was the automated segmentation of metabolically active tumour lesions on whole-body 18F-fluorodeoxyglucose PET/CT. Challenge participants had access to a large publicly available annotated PET/CT dataset for algorithm training. All algorithms submitted to the final challenge phase were based on deep learning methods, mostly using three-dimensional U-Net architectures. Submitted algorithms were evaluated on a private test set composed of 150 PET/CT studies from two institutions. An ensemble model of the highest-ranking algorithms achieved favourable performance compared with individual algorithms. Algorithm performance was dependent on the quality and quantity of data and on algorithm design choices, such as tailored post-processing of predicted segmentations. Future iterations of this challenge will focus on generalization and clinical translation. Automating the image analysis process for oncologic whole-body positron emission tomography–computed tomography data is a key area of interest. Gatidis et al. describe the autoPET 2022 challenge, an international competition focused on the segmentation of metabolically active tumour lesions, aiming to advance techniques in the field.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 11","pages":"1396-1405"},"PeriodicalIF":18.8,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142541712","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}

{"title":"Author Correction: Predicting equilibrium distributions for molecular systems with deep learning","authors":"Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu","doi":"10.1038/s42256-024-00933-4","DOIUrl":"10.1038/s42256-024-00933-4","url":null,"abstract":"","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 12","pages":"1626-1626"},"PeriodicalIF":18.8,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00933-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142845140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning prediction of ribosome profiling with Translatomer reveals translational regulation and interprets disease variants","authors":"Jialin He, Lei Xiong, Shaohui Shi, Chengyu Li, Kexuan Chen, Qianchen Fang, Jiuhong Nan, Ke Ding, Yuanhui Mao, Carles A. Boix, Xinyang Hu, Manolis Kellis, Jingyun Li, Xushen Xiong","doi":"10.1038/s42256-024-00915-6","DOIUrl":"10.1038/s42256-024-00915-6","url":null,"abstract":"Gene expression involves transcription and translation. Despite large datasets and increasingly powerful methods devoted to calculating genetic variants’ effects on transcription, discrepancy between messenger RNA and protein levels hinders the systematic interpretation of the regulatory effects of disease-associated variants. Accurate models of the sequence determinants of translation are needed to close this gap and to interpret disease-associated variants that act on translation. Here we present Translatomer, a multimodal transformer framework that predicts cell-type-specific translation from messenger RNA expression and gene sequence. We train the Translatomer on 33 tissues and cell lines, and show that the inclusion of sequence improves the prediction of ribosome profiling signal, indicating that the Translatomer captures sequence-dependent translational regulatory information. The Translatomer achieves accuracies of 0.72 to 0.80 for the de novo prediction of cell-type-specific ribosome profiling. We develop an in silico mutagenesis tool to estimate mutational effects on translation and demonstrate that variants associated with translation regulation are evolutionarily constrained, both in the human population and across species. In particular, we identify cell-type-specific translational regulatory mechanisms independent of the expression quantitative trait loci for 3,041 non-coding and synonymous variants associated with complex diseases, including Alzheimer’s disease, schizophrenia and congenital heart disease. The Translatomer accurately models the genetic underpinnings of translation, bridging the gap between messenger RNA and protein levels as well as providing valuable mechanistic insights for uninterpreted disease variants. A transformer-based approach called Translatomer is presented, which models cell-type-specific translation from messenger RNA expression and gene sequence, bridging the gap between messenger RNA and protein levels as well as providing a mechanistic insight into the genetic regulation of translation.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 11","pages":"1314-1329"},"PeriodicalIF":18.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488349","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}

{"title":"Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor–antigen recognition","authors":"Yumeng Zhang, Zhikang Wang, Yunzhe Jiang, Dene R. Littler, Mark Gerstein, Anthony W. Purcell, Jamie Rossjohn, Hong-Yu Ou, Jiangning Song","doi":"10.1038/s42256-024-00913-8","DOIUrl":"10.1038/s42256-024-00913-8","url":null,"abstract":"Understanding the mechanisms of T cell antigen recognition that underpin adaptive immune responses is critical for developing vaccines, immunotherapies and treatments against autoimmune diseases. Despite extensive research efforts, accurate prediction of T cell receptor (TCR)–antigen binding pairs remains a great challenge due to the vast diversity and cross-reactivity of TCRs. Here we propose a deep-learning-based framework termed epitope-anchored contrastive transfer learning (EPACT) tailored to paired human CD8+ TCRs. Harnessing the pretrained representations and co-embeddings of peptide–major histocompatibility complex (pMHC) and TCR, EPACT demonstrated generalizability in predicting binding specificity for unseen epitopes and distinct TCR repertoires. Contrastive learning enabled highly precise predictions for immunodominant epitopes and interpretable analysis of epitope-specific T cells. We applied EPACT to SARS-CoV-2-responsive T cells, and the predicted binding strength aligned well with the surge in spike-specific immune responses after vaccination. We further fine-tuned EPACT on structural data to decipher the residue-level interactions involved in TCR–antigen recognition. EPACT was capable of quantifying interchain distance matrices and identifying contact residues, corroborating the presence of TCR cross-reactivity across multiple tumour-associated antigens. Together, EPACT can serve as a useful artificial intelligence approach with important potential in practical applications and contribute towards the development of TCR-based immunotherapies. Accurate prediction of T cell receptor (TCR)–antigen recognition remains a challenge. Zhang et al. propose a contrastive transfer learning model to predict TCR–pMHC binding that enables interpretable analyses of epitope-specific T cells and can decipher residue-level interactions.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 11","pages":"1344-1358"},"PeriodicalIF":18.8,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486862","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}

{"title":"Pick your AI poison","authors":"","doi":"10.1038/s42256-024-00921-8","DOIUrl":"10.1038/s42256-024-00921-8","url":null,"abstract":"Distinguishing between real and fabricated facts has long been a societal challenge. As the Internet becomes increasingly littered with AI-generated content, the need for curation and safeguarding of high-quality data and information is more crucial than ever.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1119-1119"},"PeriodicalIF":18.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42256-024-00921-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging language model for advanced multiproperty molecular optimization via prompt engineering","authors":"Zhenxing Wu, Odin Zhang, Xiaorui Wang, Li Fu, Huifeng Zhao, Jike Wang, Hongyan Du, Dejun Jiang, Yafeng Deng, Dongsheng Cao, Chang-Yu Hsieh, Tingjun Hou","doi":"10.1038/s42256-024-00916-5","DOIUrl":"10.1038/s42256-024-00916-5","url":null,"abstract":"Optimizing a candidate molecule’s physiochemical and functional properties has been a critical task in drug and material design. Although the non-trivial task of balancing multiple (potentially conflicting) optimization objectives is considered ideal for artificial intelligence, several technical challenges such as the scarcity of multiproperty-labelled training data have hindered the development of a satisfactory AI solution for a long time. Prompt-MolOpt is a tool for molecular optimization; it makes use of prompt-based embeddings, as used in large language models, to improve the transformer’s ability to optimize molecules for specific property adjustments. Notably, Prompt-MolOpt excels in working with limited multiproperty data (even under the zero-shot setting) by effectively generalizing causal relationships learned from single-property datasets. In comparative evaluations against established models such as JTNN, hierG2G and Modof, Prompt-MolOpt achieves over a 15% relative improvement in multiproperty optimization success rates compared with the leading Modof model. Furthermore, a variant of Prompt-MolOpt, named Prompt-MolOptP, can preserve the pharmacophores or any user-specified fragments under the structural transformation, further broadening its application scope. By constructing tailored optimization datasets, with the protocol introduced in this work, Prompt-MolOpt steers molecular optimization towards domain-relevant chemical spaces, enhancing the quality of the optimized molecules. Real-world tests, such as those involving blood–brain barrier permeability optimization, underscore its practical relevance. Prompt-MolOpt offers a versatile approach for multiproperty and multi-site molecular optimizations, suggesting its potential utility in chemistry research and drug and material discovery. Designing molecules in drug design is challenging as it requires optimizing multiple, potentially competing qualities. Wu and colleagues present a prompt-based molecule optimization method that can be trained from single-property data.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 11","pages":"1359-1369"},"PeriodicalIF":18.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451998","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}

{"title":"Estimation of causal effects of genes on complex traits using a Bayesian-network-based framework applied to GWAS data","authors":"Liangying Yin, Yaning Feng, Yujia Shi, Alexandria Lau, Jinghong Qiu, Pak-Chung Sham, Hon-Cheong So","doi":"10.1038/s42256-024-00906-7","DOIUrl":"10.1038/s42256-024-00906-7","url":null,"abstract":"Deciphering the relationships between genes and complex traits can enhance our understanding of phenotypic variations and disease mechanisms. However, determining the specific roles of individual genes and quantifying their direct and indirect causal effects on complex traits remains a significant challenge. Here we present a framework (called Bayesian network genome-wide association studies (BN-GWAS)) to decipher the total and direct causal effects of individual genes. BN-GWAS leverages imputed expression profiles from GWAS and raw expression data from a reference dataset to construct a directed gene–gene–phenotype causal network. It allows gene expression and disease traits to be evaluated in different samples, significantly improving the flexibility and applicability of the approach. It can be extended to decipher the joint causal network of two or more traits, and exhibits high specificity and precision (positive predictive value), making it particularly useful for selecting genes for follow-up studies. We verified the feasibility and validity of BN-GWAS by extensive simulations and applications to 52 traits across 14 tissues in the UK Biobank, revealing insights into their genetic architectures, including the relative contributions of direct, indirect and mediating causal genes. The identified (direct) causal genes were significantly enriched for genes highlighted in the Open Targets database. Overall, BN-GWAS provides a flexible and powerful framework for elucidating the genetic basis of complex traits through a systems-level, causal inference approach. Genome-wide association studies generate extensive data, but interpreting these data remains challenging. A Bayesian-network-based method is presented that uses imputed and raw gene expression data to decipher the causal effects of individual genes.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 10","pages":"1231-1244"},"PeriodicalIF":18.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443839","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}

{"title":"Blending neural operators and relaxation methods in PDE numerical solvers","authors":"Enrui Zhang, Adar Kahana, Alena Kopaničáková, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis","doi":"10.1038/s42256-024-00910-x","DOIUrl":"10.1038/s42256-024-00910-x","url":null,"abstract":"Neural networks suffer from spectral bias and have difficulty representing the high-frequency components of a function, whereas relaxation methods can resolve high frequencies efficiently but stall at moderate to low frequencies. We exploit the weaknesses of the two approaches by combining them synergistically to develop a fast numerical solver of partial differential equations (PDEs) at scale. Specifically, we propose HINTS, a hybrid, iterative, numerical and transferable solver by integrating a Deep Operator Network (DeepONet) with standard relaxation methods, leading to parallel efficiency and algorithmic scalability for a wide class of PDEs, not tractable with existing monolithic solvers. HINTS balances the convergence behaviour across the spectrum of eigenmodes by utilizing the spectral bias of DeepONet, resulting in a uniform convergence rate and hence exceptional performance of the hybrid solver overall. Moreover, HINTS applies to large-scale, multidimensional systems; it is flexible with regards to discretizations, computational domain and boundary conditions; and it can also be used to precondition Krylov methods. Neural-network-based solvers for partial differential equations (PDEs) suffer from difficulties tackling high-frequency modes when learning complex functions, whereas for classical solvers it is more difficult to handle low-frequency modes. Zhang and colleagues propose a hybrid numerical PDE solver by combining a Deep Operator Network with traditional relaxation methods, leading to balanced convergence across the eigenmode spectrum for a wide range of PDEs.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"6 11","pages":"1303-1313"},"PeriodicalIF":18.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443828","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}