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

This work presents DeepSeMS, a transformer-based framework that translates biosynthetic gene clusters into candidate secondary metabolite structures, enabling scalable exploration of chemical diversity from uncultivated global ocean microbiomes.

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 development.

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 sharing. Future benchmarks should connect formal guarantees and assumptions to institutional risk, accountability, and decision making.

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

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 functional aptamers and ribozymes.

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 pairs in pulmonary fibrosis.

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 future treatments for mental health conditions.

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 faster holographic design.

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 frameworks grounded in real-world conditions and multiple material properties beyond energy and forces.

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 sharing. Future benchmarks should connect formal guarantees and assumptions to institutional risk, accountability, and decision making.

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

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

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 focuses on procedural fairness to mitigate urgent risks and align emerging technology with societal values.

Nature Computational Science now takes part in the co-review initiative in order to encourage the involvement of early-career researchers in the peer review process and to ensure proper recognition of their service.

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