Pub Date : 2025-10-10DOI: 10.1038/s43588-025-00894-7
Nature Computational Science presents a Focus that explores the field of computational psychiatry and its key challenges, from privacy concerns to the ethical use of artificial intelligence, offering new insights into the future of mental health care.
{"title":"Rethinking mental illness through a computational lens","authors":"","doi":"10.1038/s43588-025-00894-7","DOIUrl":"10.1038/s43588-025-00894-7","url":null,"abstract":"Nature Computational Science presents a Focus that explores the field of computational psychiatry and its key challenges, from privacy concerns to the ethical use of artificial intelligence, offering new insights into the future of mental health care.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"837-838"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s43588-025-00894-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256868","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 : 2025-10-10DOI: 10.1038/s43588-025-00884-9
Teddy J. Akiki, Leanne M. Williams, Thomas Wolfers, Yanwu Yang, Daniel Stahl, Claire M. Gillan
Integrating computational methods with brain-based data presents a path to precision psychiatry by capturing individual neurobiological variation, improving diagnosis, prognosis, and personalized care. This Viewpoint highlights advances in normative and foundation models, the importance of clinically grounded principles, and the role of robust measurement and interpretability in progressing mental health care.
{"title":"Transforming psychiatry with computational and brain-based methods","authors":"Teddy J. Akiki, Leanne M. Williams, Thomas Wolfers, Yanwu Yang, Daniel Stahl, Claire M. Gillan","doi":"10.1038/s43588-025-00884-9","DOIUrl":"10.1038/s43588-025-00884-9","url":null,"abstract":"Integrating computational methods with brain-based data presents a path to precision psychiatry by capturing individual neurobiological variation, improving diagnosis, prognosis, and personalized care. This Viewpoint highlights advances in normative and foundation models, the importance of clinically grounded principles, and the role of robust measurement and interpretability in progressing mental health care.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"844-847"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256870","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 : 2025-10-10DOI: 10.1038/s43588-025-00875-w
Aishik Mandal, Tanmoy Chakraborty, Iryna Gurevych
Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility. Recent advances in artificial intelligence, particularly natural language processing and multimodal methods, offer promise for detecting and addressing mental disorders. However, these innovations also introduce privacy concerns. Here we examine these challenges and propose solutions, including anonymization, synthetic data and privacy-preserving training, while outlining frameworks for privacy–utility trade-offs, aiming to advance reliable, privacy-aware artificial-intelligence tools that support clinical decision-making and improve mental health outcomes. In this Perspective, the authors examine privacy risks in mental health AI, and explore solutions and evaluation frameworks to balance privacy–utility trade-offs. They suggest a pipeline for developing privacy-aware mental health AI systems.
{"title":"Towards privacy-aware mental health AI models","authors":"Aishik Mandal, Tanmoy Chakraborty, Iryna Gurevych","doi":"10.1038/s43588-025-00875-w","DOIUrl":"10.1038/s43588-025-00875-w","url":null,"abstract":"Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility. Recent advances in artificial intelligence, particularly natural language processing and multimodal methods, offer promise for detecting and addressing mental disorders. However, these innovations also introduce privacy concerns. Here we examine these challenges and propose solutions, including anonymization, synthetic data and privacy-preserving training, while outlining frameworks for privacy–utility trade-offs, aiming to advance reliable, privacy-aware artificial-intelligence tools that support clinical decision-making and improve mental health outcomes. In this Perspective, the authors examine privacy risks in mental health AI, and explore solutions and evaluation frameworks to balance privacy–utility trade-offs. They suggest a pipeline for developing privacy-aware mental health AI systems.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"863-874"},"PeriodicalIF":18.3,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256871","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 : 2025-10-07DOI: 10.1038/s43588-025-00883-w
Kaichen Zhu, Mario Lanza
Rapid identification of pathogenic viruses remains a critical challenge. A recent study advances this frontier by demonstrating a fully integrated memristor-based hardware system that accelerates genomic analysis by a factor of 51, while reducing energy consumption to just 0.2% of that required by conventional computational methods.
{"title":"Pioneering real-time genomic analysis by in-memory computing","authors":"Kaichen Zhu, Mario Lanza","doi":"10.1038/s43588-025-00883-w","DOIUrl":"10.1038/s43588-025-00883-w","url":null,"abstract":"Rapid identification of pathogenic viruses remains a critical challenge. A recent study advances this frontier by demonstrating a fully integrated memristor-based hardware system that accelerates genomic analysis by a factor of 51, while reducing energy consumption to just 0.2% of that required by conventional computational methods.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 10","pages":"850-851"},"PeriodicalIF":18.3,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245876","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 : 2025-10-06DOI: 10.1038/s43588-025-00896-5
Yu Zheng, Fengli Xu, Yuming Lin, Paolo Santi, Carlo Ratti, Qi R. Wang, Yong Li
{"title":"Publisher Correction: Urban planning in the era of large language models","authors":"Yu Zheng, Fengli Xu, Yuming Lin, Paolo Santi, Carlo Ratti, Qi R. Wang, Yong Li","doi":"10.1038/s43588-025-00896-5","DOIUrl":"10.1038/s43588-025-00896-5","url":null,"abstract":"","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 11","pages":"1091-1091"},"PeriodicalIF":18.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s43588-025-00896-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240615","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 : 2025-10-06DOI: 10.1038/s43588-025-00881-y
Ryan K Krueger, Michael P Brenner, Krishna Shrinivas
The design of folded proteins has advanced substantially in recent years. However, many proteins and protein regions are intrinsically disordered and lack a stable fold, that is, the sequence of an intrinsically disordered protein (IDP) encodes a vast ensemble of spatial conformations that specify its biological function. This conformational plasticity and heterogeneity makes IDP design challenging. Here we introduce a computational framework for de novo design of IDPs through rational and efficient inversion of molecular simulations that approximate the underlying sequence-ensemble relationship. We highlight the versatility of this approach by designing IDPs with diverse properties and arbitrary sequence constraints. These include IDPs with target ensemble dimensions, loops and linkers, highly sensitive sensors of physicochemical stimuli, and binders to target disordered substrates with distinct conformational biases. Overall, our method provides a general framework for designing sequence-ensemble-function relationships of biological macromolecules.
{"title":"Generalized design of sequence-ensemble-function relationships for intrinsically disordered proteins.","authors":"Ryan K Krueger, Michael P Brenner, Krishna Shrinivas","doi":"10.1038/s43588-025-00881-y","DOIUrl":"https://doi.org/10.1038/s43588-025-00881-y","url":null,"abstract":"<p><p>The design of folded proteins has advanced substantially in recent years. However, many proteins and protein regions are intrinsically disordered and lack a stable fold, that is, the sequence of an intrinsically disordered protein (IDP) encodes a vast ensemble of spatial conformations that specify its biological function. This conformational plasticity and heterogeneity makes IDP design challenging. Here we introduce a computational framework for de novo design of IDPs through rational and efficient inversion of molecular simulations that approximate the underlying sequence-ensemble relationship. We highlight the versatility of this approach by designing IDPs with diverse properties and arbitrary sequence constraints. These include IDPs with target ensemble dimensions, loops and linkers, highly sensitive sensors of physicochemical stimuli, and binders to target disordered substrates with distinct conformational biases. Overall, our method provides a general framework for designing sequence-ensemble-function relationships of biological macromolecules.</p>","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":" ","pages":""},"PeriodicalIF":18.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240572","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 : 2025-10-03DOI: 10.1038/s43588-025-00887-6
Yixuan Wang, Xinyuan Liu, Yimin Fan, Binghui Xie, James Cheng, Kam Chung Wong, Peter Cheung, Irwin King, Yu Li
Drug repurposing through single-cell perturbation response prediction provides a cost-effective approach for drug development, but accurately predicting responses in unseen cell types that emerge during disease progression remains challenging. Existing methods struggle to achieve generalizable cell-type-specific predictions. To address these limitations, we introduce the cell-type-specific drug perturbatIon responses predictor (CRISP), a framework for predicting perturbation responses in previously unseen cell types at single-cell resolution. CRISP leverages foundation models and cell-type-specific learning strategies to enable effective transfer of information from control to perturbed states even with limited empirical data. Through systematic evaluation across increasingly challenging scenarios, from unseen cell types to cross-platform predictions, CRISP shows generalizability and performance improvements. We demonstrate CRISP’s drug repurposing potential through zero-shot prediction from solid tumor data to sorafenib’s therapeutic effects in chronic myeloid leukemia. The predicted anti-tumor mechanisms, including CXCR4 pathway inhibition, are supported by independent studies as an effective therapeutic strategy in chronic myeloid leukemia, aligning with past studies and clinical trials. This work develops CRISP, a framework using foundation models to predict drug responses in previously unseen cell types at single-cell resolution, advancing drug repurposing and drug screening capabilities.
{"title":"Predicting drug responses of unseen cell types through transfer learning with foundation models","authors":"Yixuan Wang, Xinyuan Liu, Yimin Fan, Binghui Xie, James Cheng, Kam Chung Wong, Peter Cheung, Irwin King, Yu Li","doi":"10.1038/s43588-025-00887-6","DOIUrl":"10.1038/s43588-025-00887-6","url":null,"abstract":"Drug repurposing through single-cell perturbation response prediction provides a cost-effective approach for drug development, but accurately predicting responses in unseen cell types that emerge during disease progression remains challenging. Existing methods struggle to achieve generalizable cell-type-specific predictions. To address these limitations, we introduce the cell-type-specific drug perturbatIon responses predictor (CRISP), a framework for predicting perturbation responses in previously unseen cell types at single-cell resolution. CRISP leverages foundation models and cell-type-specific learning strategies to enable effective transfer of information from control to perturbed states even with limited empirical data. Through systematic evaluation across increasingly challenging scenarios, from unseen cell types to cross-platform predictions, CRISP shows generalizability and performance improvements. We demonstrate CRISP’s drug repurposing potential through zero-shot prediction from solid tumor data to sorafenib’s therapeutic effects in chronic myeloid leukemia. The predicted anti-tumor mechanisms, including CXCR4 pathway inhibition, are supported by independent studies as an effective therapeutic strategy in chronic myeloid leukemia, aligning with past studies and clinical trials. This work develops CRISP, a framework using foundation models to predict drug responses in previously unseen cell types at single-cell resolution, advancing drug repurposing and drug screening capabilities.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"6 1","pages":"39-52"},"PeriodicalIF":18.3,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226337","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 : 2025-10-03DOI: 10.1038/s43588-025-00880-z
Kunyi Li, Baozhen Shan, Lei Xin, Ming Li, Lusheng Wang
Here we propose a search algorithm for proteoform identification that computes the largest-size error-correction alignments between a protein mass graph and a spectrum mass graph. Our combined method uses a filtering algorithm to identify candidates and then applies a search algorithm to report the final results. Our exact searching method is 3.9 to 9.0 times faster than popular methods such as TopMG and TopPIC. Our combined method can further speed-up the running time of sTopMG without affecting the search accuracy. We develop a pipeline for generating simulated top-down spectra on the basis of input protein sequences with modifications. Experiments on simulated datasets show that our combined method has 95% accuracy, which exceeds existing methods. Experiments on real annotated datasets show that our method has ≥97.1% accuracy using deconvolution method FLASHDeconv. An algorithm for proteoform identification with top-down mass spectra is proposed, and a pipeline is developed for generating simulated top-down spectra on the basis of input protein sequences with modifications.
{"title":"Proteoform search from protein database with top-down mass spectra","authors":"Kunyi Li, Baozhen Shan, Lei Xin, Ming Li, Lusheng Wang","doi":"10.1038/s43588-025-00880-z","DOIUrl":"10.1038/s43588-025-00880-z","url":null,"abstract":"Here we propose a search algorithm for proteoform identification that computes the largest-size error-correction alignments between a protein mass graph and a spectrum mass graph. Our combined method uses a filtering algorithm to identify candidates and then applies a search algorithm to report the final results. Our exact searching method is 3.9 to 9.0 times faster than popular methods such as TopMG and TopPIC. Our combined method can further speed-up the running time of sTopMG without affecting the search accuracy. We develop a pipeline for generating simulated top-down spectra on the basis of input protein sequences with modifications. Experiments on simulated datasets show that our combined method has 95% accuracy, which exceeds existing methods. Experiments on real annotated datasets show that our method has ≥97.1% accuracy using deconvolution method FLASHDeconv. An algorithm for proteoform identification with top-down mass spectra is proposed, and a pipeline is developed for generating simulated top-down spectra on the basis of input protein sequences with modifications.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 11","pages":"998-1009"},"PeriodicalIF":18.3,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226292","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 : 2025-10-02DOI: 10.1038/s43588-025-00892-9
We propose a computationally efficient genome-wide association study (GWAS) method, WtCoxG, for time-to-event (TTE) traits in the presence of case ascertainment— a form of oversampling bias. WtCoxG addresses case ascertainment bias by applying a weighted Cox proportional hazard model, and outperforms existing approaches when incorporating information on external allele frequencies.
{"title":"Boosting power for time-to-event GWAS analysis affected by case ascertainment","authors":"","doi":"10.1038/s43588-025-00892-9","DOIUrl":"10.1038/s43588-025-00892-9","url":null,"abstract":"We propose a computationally efficient genome-wide association study (GWAS) method, WtCoxG, for time-to-event (TTE) traits in the presence of case ascertainment— a form of oversampling bias. WtCoxG addresses case ascertainment bias by applying a weighted Cox proportional hazard model, and outperforms existing approaches when incorporating information on external allele frequencies.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 11","pages":"996-997"},"PeriodicalIF":18.3,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214719","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 : 2025-10-01DOI: 10.1038/s43588-025-00885-8
Evan Collins, Robert Langer, Daniel G. Anderson
Self-driving laboratories that integrate robotic production with artificial intelligence have the potential to accelerate innovation in biotechnology. Because self-driving labs can be complex and not universally applicable, it is useful to consider their suitable use cases for successful integration into discovery workflows. Here, we review strategies for assessing the suitability of self-driving labs for biochemical design problems.
{"title":"Self-driving labs for biotechnology","authors":"Evan Collins, Robert Langer, Daniel G. Anderson","doi":"10.1038/s43588-025-00885-8","DOIUrl":"10.1038/s43588-025-00885-8","url":null,"abstract":"Self-driving laboratories that integrate robotic production with artificial intelligence have the potential to accelerate innovation in biotechnology. Because self-driving labs can be complex and not universally applicable, it is useful to consider their suitable use cases for successful integration into discovery workflows. Here, we review strategies for assessing the suitability of self-driving labs for biochemical design problems.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 11","pages":"976-979"},"PeriodicalIF":18.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208389","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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