SpatialFormer: universal spatial representation learning from subcellular molecular to multicellular landscapes - Nature Computational Science

SpatialFormer, a transformer-based model for spatial transcriptomics, learns cell–cell relationships to predict cell co-localization, annotate cell types and niches, and reveal cell communication gene...

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Structure-alignment-driven cross-graph modeling for functional RNA design - Nature Computational Science

This study presents AlignIF, which uses multiple structure alignments to capture evolutionary rules and conservation patterns across RNA families, enabling diverse, structure-guided designs of functio...

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Continual integration of single-cell multimodal data with MIRACLE - Nature Computational Science

MIRACLE is an online continual learning framework that efficiently integrates single-cell multimodal data across batches, modalities, tissues, and diseases.

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Rethinking privacy-preserving GWAS benchmarks for governance - Nature Computational Science

Privacy, utility, and efficiency benchmarks have advanced privacy-preserving genome-wide association studies (GWAS) but often fall short of supporting organizational decisions about responsible data s...

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Cross-task, explainable and real-time decoding of human emotion states by integrating gray and white matter intracranial neural activity - Nature Computational Science

This study reports personalized models that decode human emotion states from intracranial brain recordings in real time, work across different tasks and uncover key brain networks that could guide fut...

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AlphaFold2 turns five - Nature Computational Science

We highlight how AlphaFold2 has advanced protein design and inspired the development of artificial intelligence-driven infrastructures for scientific discovery.

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UniFFBench: evaluating universal machine learning force fields against experimental measurements - Nature Computational Science

Universal machine learning force fields excel on computational benchmarks but consistently fail when tested against experimental measurements of real minerals, underscoring the need for evaluation fra...

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Generative model for information metamaterial design - Nature Computational Science

A generative model designs information metamaterials from meta-atoms to programmable arrays, enabling beam steering, focusing and holography with experimental validation and more than 1,000-fold faste...

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Understanding language model scaling for protein fitness prediction - Nature Computational Science

Larger language models do not always perform better at fitness prediction. Hou et al. found that performance depends on whether model outputs match evolutionary patterns in homologs, which is best ach...

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Next-Generation Digital Twins for Drug Discovery

With this Collection, the editors invite contributions that advance the foundations and applications of high-fidelity digital twins in drug discovery.

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Empowering neural network-based quantum Monte Carlo with local pseudopotentials - Nature Computational Science

This study shows that local pseudopotentials boost the efficiency of neural network-based quantum Monte Carlo while preserving accuracy, enabling reliable simulations of large, challenging iron–sulfur...

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Integrating cytological images and spatial transcriptomics for cell segmentation with DISSECT - Nature Computational Science

The authors propose a framework that integrates cytological imaging and spatial transcriptomics to improve cell segmentation and enable reconstruction of spatial single-cell transcriptomes.

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Ultrafast and ultralarge distance-based phylogenetics using DIPPER - Nature Computational Science

This study introduces DIPPER, a GPU-accelerated tool that efficiently reconstructs evolutionary trees from tens of millions of genome sequences, extending scalability by one to two orders of magnitude...

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NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements - Nature Computational Science

The researchers developed NEP89, a foundation model for large-scale molecular dynamics across 89 elements with near-quantum-level accuracy and high efficiency. It can be rapidly adapted to address com...

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Computing inspired by the brain: a journey from algorithms to organoids - Nature Computational Science

Organoid intelligence represents a new frontier for computing inspired by the human brain. This Review discusses the evolution of brain-inspired computing along with potential challenges for future de...

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Systematic bottom-up coarse-graining of hydrated excess proton transport across scales - Nature Computational Science

HUMID is a bottom-up coarse-grained framework for proton transport that bridges the quantum and molecular scales, enabling simulations of reactive transport dynamics across diverse environments with o...

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Gaining biological insights through supervised data visualization - Nature Computational Science

This study introduces RF-PHATE, a supervised visualization method that preserves data structure while revealing biological patterns, enabling insights into disease progression in multiple sclerosis, C...

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Social technologies need societal alignment - Nature Computational Science

Although social technologies are increasingly co-shaping the public sphere, these systems were not designed as democratic infrastructure. Here, we propose a framework for societal alignment that focus...

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The inequalities of GPU access - Nature Computational Science

Geographical disparities regarding the availability of GPU hardware are becoming a structural constraint on scientific participation itself and must be addressed.

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The Quantum Optimization Benchmarking Library - Nature Computational Science

This Resource presents the Quantum Optimization Benchmarking Library, which enables fair, reproducible benchmarks of quantum heuristics for ten difficult combinatorial optimization classes with baseli...

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Evidence of scaling advantage on an NP-complete problem with enhanced quantum solvers - Nature Computational Science

This study develops enhanced quantum solvers for an NP-complete Boolean satisfiability problem and reports empirical scaling advantages over leading classical solvers in large-scale simulations, with ...

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Leveraging longitudinal data to boost statistical power for gene–environment interaction analysis - Nature Computational Science

The authors introduce SAGELD, a method that leverages longitudinal data to detect gene–environment interactions with greater statistical power than conventional approaches based on cross-sectional dat...

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Transferable human mobility network reconstruction with neuroGravity - Nature Computational Science

This study presents neuroGravity—a model that merges physics with machine learning to reconstruct mobility networks in data-scarce regions. It provides crucial tools for equitable urban planning, reve...

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MetaSTAARlite: an all-in-one tool for biobank-scale whole-genome sequencing meta-analysis - Nature Computational Science

MetaSTAARlite provides a scalable and resource-efficient summary statistics-based pipeline for powerful, functionally informed rare-variant meta-analysis of biobank-scale sequencing data.

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The Open Materials 2024 (OMat24) inorganic materials dataset and models - Nature Computational Science

This Resource introduces the Open Materials 2024 (OMat24) dataset, offering more than 110 million diverse density functional theory calculations. AI models trained on OMat24 achieve a high accuracy an...

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Protein language models for structural biology - Nature Computational Science

Protein language models can effectively decode evolution’s grammar, making structure prediction and design scalable. This transformative capability is accelerating biological discovery and engineering...

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