Pub Date : 2025-12-31DOI: 10.1038/s42256-025-01166-9
Remmy Zen, Maximilian Nägele, Florian Marquardt
Quantum computing has the potential to solve problems that are intractable for classical computers, with possible applications in areas such as drug discovery and high-energy physics. However, the practical implementation of quantum computation is hindered by the complexity of executing quantum circuits on hardware. In particular, minimizing the number of T gates is crucial for implementing efficient quantum algorithms. AlphaTensor-Quantum1 is a reinforcement-learning-based method designed to optimize the T count of quantum circuits by formulating the problem as a tensor decomposition task. Although it has demonstrated superior performance over existing methods on benchmark quantum arithmetic circuits, its applicability has so far been restricted to specific circuit families, requiring separate, time-intensive training for each new application. This report reproduces some of the key results of the original work and extends AlphaTensor-Quantum’s capabilities to simplify random quantum circuits with varying qubit counts, eliminating the need for retraining on new circuits. Our experiments show that a general agent trained on five- to eight-qubit circuits achieves greater T-count reduction than previous methods for a large fraction of quantum circuits. Furthermore, we demonstrate that a general agent trained on circuits with varying qubit numbers outperforms agents trained on fixed qubit numbers, highlighting the method’s generalizability and its potential for broader quantum circuit optimization tasks. The reusability of AlphaTensor-Quantum is tested and the method is extended to optimize a broad range of quantum circuits without retraining, achieving greater T-count reductions and demonstrating generalizable and efficient quantum circuit optimization.
{"title":"Reusability report: Optimizing T count in general quantum circuits with AlphaTensor-Quantum","authors":"Remmy Zen, Maximilian Nägele, Florian Marquardt","doi":"10.1038/s42256-025-01166-9","DOIUrl":"10.1038/s42256-025-01166-9","url":null,"abstract":"Quantum computing has the potential to solve problems that are intractable for classical computers, with possible applications in areas such as drug discovery and high-energy physics. However, the practical implementation of quantum computation is hindered by the complexity of executing quantum circuits on hardware. In particular, minimizing the number of T gates is crucial for implementing efficient quantum algorithms. AlphaTensor-Quantum1 is a reinforcement-learning-based method designed to optimize the T count of quantum circuits by formulating the problem as a tensor decomposition task. Although it has demonstrated superior performance over existing methods on benchmark quantum arithmetic circuits, its applicability has so far been restricted to specific circuit families, requiring separate, time-intensive training for each new application. This report reproduces some of the key results of the original work and extends AlphaTensor-Quantum’s capabilities to simplify random quantum circuits with varying qubit counts, eliminating the need for retraining on new circuits. Our experiments show that a general agent trained on five- to eight-qubit circuits achieves greater T-count reduction than previous methods for a large fraction of quantum circuits. Furthermore, we demonstrate that a general agent trained on circuits with varying qubit numbers outperforms agents trained on fixed qubit numbers, highlighting the method’s generalizability and its potential for broader quantum circuit optimization tasks. The reusability of AlphaTensor-Quantum is tested and the method is extended to optimize a broad range of quantum circuits without retraining, achieving greater T-count reductions and demonstrating generalizable and efficient quantum circuit optimization.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 1","pages":"113-117"},"PeriodicalIF":23.9,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s42256-025-01166-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894628","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}
Pub Date : 2025-12-31DOI: 10.1038/s42256-025-01170-z
Fei He, Ruixin Fei, Jordan E. Krull, Yang Yu, Xinyu Zhang, Xianyu Wang, Hao Cheng, Mingyue Gao, Li Su, Yibo Chen, Jinpu Li, Baichuan Jin, Yuzhou Chang, Anjun Ma, Qin Ma, Dong Xu
Single-cell large language models (scLLMs) capture essential biological insights from vast single-cell atlases but struggle in out-of-context applications, where zero-shot predictions can be unreliable. To address this, here we introduce a single-cell parameter-efficient fine-tuning (scPEFT) framework that integrates learnable, low-dimensional adapters into scLLMs. By freezing the backbone model and updating only the adapter parameters, scPEFT efficiently adapts to specific tasks using limited custom data. This approach mitigates catastrophic forgetting, reduces parameter tuning by over 96% and decreases GPU memory usage by more than half, thus substantially enhancing the accessibility of scLLMs for resource-constrained researchers. When validated across diverse datasets, scPEFT outperformed zero-shot models and traditional fine-tuning in disease-specific, cross-species and undercharacterized cell population tasks. Its attention-mechanism analysis identified COVID-related genes associated with specific cell states and uncovered unique blood cell subpopulations, demonstrating the capacity of scPEFT for condition-specific interpretations. These findings position scPEFT as an efficient solution for enhancing the utility of scLLMs in general single-cell analyses. He et al. present a parameter-efficient fine-tuning method for single-cell language models that improves performance on unseen diseases, treatments and cell types.
{"title":"Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT","authors":"Fei He, Ruixin Fei, Jordan E. Krull, Yang Yu, Xinyu Zhang, Xianyu Wang, Hao Cheng, Mingyue Gao, Li Su, Yibo Chen, Jinpu Li, Baichuan Jin, Yuzhou Chang, Anjun Ma, Qin Ma, Dong Xu","doi":"10.1038/s42256-025-01170-z","DOIUrl":"10.1038/s42256-025-01170-z","url":null,"abstract":"Single-cell large language models (scLLMs) capture essential biological insights from vast single-cell atlases but struggle in out-of-context applications, where zero-shot predictions can be unreliable. To address this, here we introduce a single-cell parameter-efficient fine-tuning (scPEFT) framework that integrates learnable, low-dimensional adapters into scLLMs. By freezing the backbone model and updating only the adapter parameters, scPEFT efficiently adapts to specific tasks using limited custom data. This approach mitigates catastrophic forgetting, reduces parameter tuning by over 96% and decreases GPU memory usage by more than half, thus substantially enhancing the accessibility of scLLMs for resource-constrained researchers. When validated across diverse datasets, scPEFT outperformed zero-shot models and traditional fine-tuning in disease-specific, cross-species and undercharacterized cell population tasks. Its attention-mechanism analysis identified COVID-related genes associated with specific cell states and uncovered unique blood cell subpopulations, demonstrating the capacity of scPEFT for condition-specific interpretations. These findings position scPEFT as an efficient solution for enhancing the utility of scLLMs in general single-cell analyses. He et al. present a parameter-efficient fine-tuning method for single-cell language models that improves performance on unseen diseases, treatments and cell types.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 1","pages":"118-133"},"PeriodicalIF":23.9,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894629","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}
Pub Date : 2025-12-31DOI: 10.1038/s42256-025-01163-y
Kevin Bijan Givechian, João Felipe Rocha, Chen Liu, Edward Yang, Sidharth Tyagi, Kerrie Greene, Rex Ying, Etienne Caron, Akiko Iwasaki, Smita Krishnaswamy
Epitope-based vaccines are promising therapeutic modalities for infectious diseases and cancer, but identifying immunogenic epitopes is challenging. Most prediction methods only use amino acid sequence information, and do not incorporate wide-scale structure data and biochemical properties across each peptide–major histocompatibility complex (MHC). We present ImmunoStruct, a deep learning model that integrates sequence, structural and biochemical information to predict multi-allele class I peptide–MHC immunogenicity. By leveraging a multimodal dataset of 26,049 peptide–MHCs, we demonstrate that ImmunoStruct improves immunogenicity prediction performance and interpretability beyond existing methods, across infectious disease epitopes and cancer neoepitopes. We further show strong alignment with in vitro assay results for a set of SARS-CoV-2 epitopes, as well as strong performance in peptide–MHC-based survival prediction for patients with cancer. Overall, this work also presents an architecture that incorporates equivariant graph processing and multimodal data integration for a long-standing challenge in immunotherapy. A multimodal deep learning model combines molecular sequence, structure and biochemical properties to predict immunogenicity in an interpretable way, providing a framework for smarter molecular prediction and hypothesis generation.
{"title":"ImmunoStruct enables multimodal deep learning for immunogenicity prediction","authors":"Kevin Bijan Givechian, João Felipe Rocha, Chen Liu, Edward Yang, Sidharth Tyagi, Kerrie Greene, Rex Ying, Etienne Caron, Akiko Iwasaki, Smita Krishnaswamy","doi":"10.1038/s42256-025-01163-y","DOIUrl":"10.1038/s42256-025-01163-y","url":null,"abstract":"Epitope-based vaccines are promising therapeutic modalities for infectious diseases and cancer, but identifying immunogenic epitopes is challenging. Most prediction methods only use amino acid sequence information, and do not incorporate wide-scale structure data and biochemical properties across each peptide–major histocompatibility complex (MHC). We present ImmunoStruct, a deep learning model that integrates sequence, structural and biochemical information to predict multi-allele class I peptide–MHC immunogenicity. By leveraging a multimodal dataset of 26,049 peptide–MHCs, we demonstrate that ImmunoStruct improves immunogenicity prediction performance and interpretability beyond existing methods, across infectious disease epitopes and cancer neoepitopes. We further show strong alignment with in vitro assay results for a set of SARS-CoV-2 epitopes, as well as strong performance in peptide–MHC-based survival prediction for patients with cancer. Overall, this work also presents an architecture that incorporates equivariant graph processing and multimodal data integration for a long-standing challenge in immunotherapy. A multimodal deep learning model combines molecular sequence, structure and biochemical properties to predict immunogenicity in an interpretable way, providing a framework for smarter molecular prediction and hypothesis generation.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 1","pages":"70-83"},"PeriodicalIF":23.9,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895500","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: Scalable and robust DNA-based storage via coding theory and deep learning","authors":"Daniella Bar-Lev, Itai Orr, Omer Sabary, Tuvi Etzion, Eitan Yaakobi","doi":"10.1038/s42256-025-01175-8","DOIUrl":"10.1038/s42256-025-01175-8","url":null,"abstract":"","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 1","pages":"134-134"},"PeriodicalIF":23.9,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s42256-025-01175-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894634","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}
Pub Date : 2025-12-29DOI: 10.1038/s42256-025-01147-y
Zehua Chen, Yuyang Miao, Liyuan Wang, Luyun Fan, Danilo P. Mandic, Jun Zhu
Cardiovascular signals such as photoplethysmography, electrocardiography and blood pressure are inherently correlated and complementary, together reflecting the health of the cardiovascular system. However, their joint utilization in real-time monitoring is severely limited by diverse acquisition challenges from noisy wearable recordings to burdened invasive procedures. Here we propose UniCardio, a multimodal diffusion transformer that reconstructs low-quality signals and synthesizes unrecorded signals in a unified generative framework. Its key innovations include a specialized model architecture to manage the signal modalities involved in generation tasks and a continual learning paradigm to incorporate varying modality combinations. By exploiting the complementary nature of cardiovascular signals, UniCardio clearly outperforms recent task-specific baselines in signal denoising, imputation and translation. The generated signals match the performance of ground-truth signals in detecting abnormal health conditions and estimating vital signs, even in unseen domains, as well as ensuring interpretability for human experts. These advantages establish UniCardio as a practical and robust framework for advancing artificial-intelligence-assisted healthcare. UniCardio is a unified framework for versatile multimodal cardiovascular signal generation, enabling robust signal restoration and cross-modal translation to detect abnormal conditions and estimate vital signs in real-time health monitoring.
{"title":"Versatile cardiovascular signal generation with a unified diffusion transformer","authors":"Zehua Chen, Yuyang Miao, Liyuan Wang, Luyun Fan, Danilo P. Mandic, Jun Zhu","doi":"10.1038/s42256-025-01147-y","DOIUrl":"10.1038/s42256-025-01147-y","url":null,"abstract":"Cardiovascular signals such as photoplethysmography, electrocardiography and blood pressure are inherently correlated and complementary, together reflecting the health of the cardiovascular system. However, their joint utilization in real-time monitoring is severely limited by diverse acquisition challenges from noisy wearable recordings to burdened invasive procedures. Here we propose UniCardio, a multimodal diffusion transformer that reconstructs low-quality signals and synthesizes unrecorded signals in a unified generative framework. Its key innovations include a specialized model architecture to manage the signal modalities involved in generation tasks and a continual learning paradigm to incorporate varying modality combinations. By exploiting the complementary nature of cardiovascular signals, UniCardio clearly outperforms recent task-specific baselines in signal denoising, imputation and translation. The generated signals match the performance of ground-truth signals in detecting abnormal health conditions and estimating vital signs, even in unseen domains, as well as ensuring interpretability for human experts. These advantages establish UniCardio as a practical and robust framework for advancing artificial-intelligence-assisted healthcare. UniCardio is a unified framework for versatile multimodal cardiovascular signal generation, enabling robust signal restoration and cross-modal translation to detect abnormal conditions and estimate vital signs in real-time health monitoring.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 1","pages":"6-19"},"PeriodicalIF":23.9,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895542","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}
Pub Date : 2025-12-18DOI: 10.1038/s42256-025-01150-3
Michael E. Vinyard, Anders W. Rasmussen, Ruitong Li, Allon M. Klein, Gad Getz, Luca Pinello
Single-cell sequencing measurements facilitate the reconstruction of dynamic biology by capturing snapshot molecular profiles of individual cells. Cell fate decisions in development and disease are orchestrated through an intricate balance of deterministic and stochastic regulatory events. Drift-diffusion equations are effective in modelling single-cell dynamics from high-dimensional single-cell measurements. While existing solutions describe the deterministic dynamics associated with the drift term of these equations at the level of cell state, diffusion is modelled as a constant across cell states. To fully understand the dynamic regulatory logic in development and disease, models explicitly attuned to the balance between deterministic and stochastic biology are required. To address these limitations, we introduce scDiffEq, a generative framework for learning neural stochastic differential equations that approximate biology’s deterministic and stochastic dynamics. Using lineage-traced single-cell data, we demonstrate that scDiffEq offers an improved reconstruction of cell trajectories and prediction of cell fate from multipotent progenitors during haematopoiesis. By imparting in silico perturbations to multipotent progenitor cells, we find that scDiffEq accurately recapitulates the dynamics of CRISPR-perturbed haematopoiesis. We generalize this approach beyond lineage-traced or multi-time-point datasets to model the dynamics of single-cell data from a single time point. Using scDiffEq, we simulate high-resolution developmental cell trajectories, which can model their drift and diffusion, enabling us to study their time-dependent gene-level dynamics. Vinyard et al. present a generative method to model cell dynamics using neural stochastic differential equations that learn state-dependent drift and diffusion, outperforming existing approaches and enabling perturbation studies of development and disease.
{"title":"Learning cell dynamics with neural differential equations","authors":"Michael E. Vinyard, Anders W. Rasmussen, Ruitong Li, Allon M. Klein, Gad Getz, Luca Pinello","doi":"10.1038/s42256-025-01150-3","DOIUrl":"10.1038/s42256-025-01150-3","url":null,"abstract":"Single-cell sequencing measurements facilitate the reconstruction of dynamic biology by capturing snapshot molecular profiles of individual cells. Cell fate decisions in development and disease are orchestrated through an intricate balance of deterministic and stochastic regulatory events. Drift-diffusion equations are effective in modelling single-cell dynamics from high-dimensional single-cell measurements. While existing solutions describe the deterministic dynamics associated with the drift term of these equations at the level of cell state, diffusion is modelled as a constant across cell states. To fully understand the dynamic regulatory logic in development and disease, models explicitly attuned to the balance between deterministic and stochastic biology are required. To address these limitations, we introduce scDiffEq, a generative framework for learning neural stochastic differential equations that approximate biology’s deterministic and stochastic dynamics. Using lineage-traced single-cell data, we demonstrate that scDiffEq offers an improved reconstruction of cell trajectories and prediction of cell fate from multipotent progenitors during haematopoiesis. By imparting in silico perturbations to multipotent progenitor cells, we find that scDiffEq accurately recapitulates the dynamics of CRISPR-perturbed haematopoiesis. We generalize this approach beyond lineage-traced or multi-time-point datasets to model the dynamics of single-cell data from a single time point. Using scDiffEq, we simulate high-resolution developmental cell trajectories, which can model their drift and diffusion, enabling us to study their time-dependent gene-level dynamics. Vinyard et al. present a generative method to model cell dynamics using neural stochastic differential equations that learn state-dependent drift and diffusion, outperforming existing approaches and enabling perturbation studies of development and disease.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 12","pages":"1969-1984"},"PeriodicalIF":23.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770795","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}
Pub Date : 2025-12-18DOI: 10.1038/s42256-025-01115-6
Gregory Serapio-García, Mustafa Safdari, Clément Crepy, Luning Sun, Stephen Fitz, Peter Romero, Marwa Abdulhai, Aleksandra Faust, Maja Matarić
The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text. As LLMs increasingly power conversational agents used by the general public worldwide, the synthetic personality traits embedded in these models by virtue of training on large amounts of human data are becoming increasingly important to evaluate. The style in which LLMs respond can mimic different human personality traits. Here, as these patterns can be a key factor determining the effectiveness of communication, we present a comprehensive psychometric methodology for administering and validating personality tests on widely used LLMs, as well as for shaping personality in the generated text of such LLMs. Applying this method to 18 LLMs, we found that: personality measurements in the outputs of some LLMs under specific prompting configurations are reliable and valid; evidence of reliability and validity of synthetic LLM personality is stronger for larger and instruction-fine-tuned models; and personality in LLM outputs can be shaped along desired dimensions to mimic specific human personality profiles. We discuss the application and ethical implications of the measurement and shaping method, in particular regarding responsible artificial intelligence. Serapio-García, Safdari and colleagues develop a method based on psychometric tests to measure and validate personality-like traits in LLMs. Large, instruction-tuned models give reliable personality measurement results, and specific personality profiles can be mimicked in downstream tasks.
{"title":"A psychometric framework for evaluating and shaping personality traits in large language models","authors":"Gregory Serapio-García, Mustafa Safdari, Clément Crepy, Luning Sun, Stephen Fitz, Peter Romero, Marwa Abdulhai, Aleksandra Faust, Maja Matarić","doi":"10.1038/s42256-025-01115-6","DOIUrl":"10.1038/s42256-025-01115-6","url":null,"abstract":"The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text. As LLMs increasingly power conversational agents used by the general public worldwide, the synthetic personality traits embedded in these models by virtue of training on large amounts of human data are becoming increasingly important to evaluate. The style in which LLMs respond can mimic different human personality traits. Here, as these patterns can be a key factor determining the effectiveness of communication, we present a comprehensive psychometric methodology for administering and validating personality tests on widely used LLMs, as well as for shaping personality in the generated text of such LLMs. Applying this method to 18 LLMs, we found that: personality measurements in the outputs of some LLMs under specific prompting configurations are reliable and valid; evidence of reliability and validity of synthetic LLM personality is stronger for larger and instruction-fine-tuned models; and personality in LLM outputs can be shaped along desired dimensions to mimic specific human personality profiles. We discuss the application and ethical implications of the measurement and shaping method, in particular regarding responsible artificial intelligence. Serapio-García, Safdari and colleagues develop a method based on psychometric tests to measure and validate personality-like traits in LLMs. Large, instruction-tuned models give reliable personality measurement results, and specific personality profiles can be mimicked in downstream tasks.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 12","pages":"1954-1968"},"PeriodicalIF":23.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s42256-025-01115-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770796","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}
Pub Date : 2025-12-18DOI: 10.1038/s42256-025-01151-2
Amitesh Badkul, Li Xie, Shuo Zhang, Lei Xie
Polypharmacology, a single drug that targets multiple proteins, holds promise for addressing unmet medical needs. Achieving accurate, reliable and scalable predictions of protein–ligand binding affinity across multiple proteins is crucial to realizing the potential of polypharmacology. Machine learning offers a powerful tool for multitarget binding affinity prediction. However, three major challenges remain: generalizing predictions to out-of-distribution compounds that are structurally different from those in the training data; quantifying the uncertainty of predictions in out-of-distribution scenarios where the assumption underlying existing methods does not hold; and scaling to billions of compounds, which remains unattainable for current structure-based methods. Here, to overcome these challenges, we propose a model-agnostic anomaly detection-based individual uncertainty quantification method: embedding Mahalanobis Outlier Scoring and Anomaly Identification via Clustering (eMOSAIC). eMOSAIC features the divergence between the multimodal representations of known cases and unseen instances and quantifies individual prediction uncertainty on a compound-by-compound basis. We integrate eMOSAIC with a multimodal deep neural network for multitarget ligand binding affinity predictions, leveraging a structure-informed large protein language model. Comprehensive validation in out-of-distribution settings demonstrates that eMOSAIC significantly outperforms state-of-the-art sequence-based and structure-based methods as well as existing uncertainty quantification approaches. These findings underscore eMOSAIC’s potential to advance real-world polypharmacology and other applications that require robust predictions and scalable solutions. Badkul et al. develop eMOSAIC, a method that improves drug discovery by accurately predicting the interaction mechanics of various compounds with multiple proteins.
{"title":"Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology","authors":"Amitesh Badkul, Li Xie, Shuo Zhang, Lei Xie","doi":"10.1038/s42256-025-01151-2","DOIUrl":"10.1038/s42256-025-01151-2","url":null,"abstract":"Polypharmacology, a single drug that targets multiple proteins, holds promise for addressing unmet medical needs. Achieving accurate, reliable and scalable predictions of protein–ligand binding affinity across multiple proteins is crucial to realizing the potential of polypharmacology. Machine learning offers a powerful tool for multitarget binding affinity prediction. However, three major challenges remain: generalizing predictions to out-of-distribution compounds that are structurally different from those in the training data; quantifying the uncertainty of predictions in out-of-distribution scenarios where the assumption underlying existing methods does not hold; and scaling to billions of compounds, which remains unattainable for current structure-based methods. Here, to overcome these challenges, we propose a model-agnostic anomaly detection-based individual uncertainty quantification method: embedding Mahalanobis Outlier Scoring and Anomaly Identification via Clustering (eMOSAIC). eMOSAIC features the divergence between the multimodal representations of known cases and unseen instances and quantifies individual prediction uncertainty on a compound-by-compound basis. We integrate eMOSAIC with a multimodal deep neural network for multitarget ligand binding affinity predictions, leveraging a structure-informed large protein language model. Comprehensive validation in out-of-distribution settings demonstrates that eMOSAIC significantly outperforms state-of-the-art sequence-based and structure-based methods as well as existing uncertainty quantification approaches. These findings underscore eMOSAIC’s potential to advance real-world polypharmacology and other applications that require robust predictions and scalable solutions. Badkul et al. develop eMOSAIC, a method that improves drug discovery by accurately predicting the interaction mechanics of various compounds with multiple proteins.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 12","pages":"1985-1995"},"PeriodicalIF":23.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771625","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}
Pub Date : 2025-12-17DOI: 10.1038/s42256-025-01158-9
Wenhao Song, Zixu Wang, J. Joshua Yang
Brain-inspired computing can enhance the finite element method, a cornerstone of scientific modelling, by reducing energy costs and reframing numerical simulation through neural dynamics.
{"title":"Solving finite element methods with spiking networks","authors":"Wenhao Song, Zixu Wang, J. Joshua Yang","doi":"10.1038/s42256-025-01158-9","DOIUrl":"10.1038/s42256-025-01158-9","url":null,"abstract":"Brain-inspired computing can enhance the finite element method, a cornerstone of scientific modelling, by reducing energy costs and reframing numerical simulation through neural dynamics.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"7 12","pages":"1891-1892"},"PeriodicalIF":23.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770797","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}
Pub Date : 2025-12-17DOI: 10.1038/s42256-025-01156-x
Parker Grosjean, Kaivalya Shevade, Cuong Nguyen, Sarah Ancheta, Karl Mader, Ivan Franco, Seok-Jin Heo, Greyson Lewis, Dehua Zhao, Bhairavi Tolani, Steven Boggess, Angelique Di Domenico, Erik Ullian, Shawn Shafer, Adam Litterman, Laralynne Przybyla, Michael J. Keiser, Jamie Ifkovits, Adam Yala, Martin Kampmann
High-throughput phenotypic screening has historically relied on manually selected features, limiting our ability to capture complex cellular processes, particularly neuronal activity dynamics. While recent advances in self-supervised learning have revolutionized the study of cellular morphology and transcriptomics, dynamic cellular processes remain challenging to phenotypically profile. To address this, we developed Plexus, a self-supervised model designed to capture and quantify network-level neuronal activity. Unlike existing tools that focus on static readouts, Plexus leverages a network-level cell encoding method, efficiently encoding dynamic neuronal activity into rich representational embeddings. In turn, Plexus achieves state-of-the-art performance in detecting phenotypic changes in neuronal activity. Here we validated Plexus using a comprehensive GCaMP6m simulation framework and demonstrated its ability to classify distinct phenotypes compared with traditional signal-processing approaches. To enable practical application, we integrated Plexus with a scalable experimental system using human induced pluripotent stem cell-derived neurons expressing the GCaMP6m calcium indicator and CRISPR interference machinery. This platform successfully identified nearly 17 times as many phenotypic changes in response to genetic perturbations compared with conventional methods, as demonstrated in a 52-gene CRISPR interference screen across multiple induced pluripotent stem cell lines. Using this framework, we identified potential genetic modifiers of aberrant neuronal activity in frontotemporal dementia, illustrating its utility for understanding complex neurological disorders. Grosjean et al. present a network-aware, self-supervised learning approach for screening neuronal activity dynamics. They demonstrate its applicability across a range of neural interventions.
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