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.

FLOWR combines continuous and categorical flow matching with equivariant optimal transport and a separate protein pocket encoder to improve validity, pose accuracy and interaction recovery at substantially faster inference speeds.

This Resource presents the Quantum Optimization Benchmarking Library, which enables fair, reproducible benchmarks of quantum heuristics for ten difficult combinatorial optimization classes with baseline results to track progress towards quantum advantage.

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 over 1,000-fold greater computational efficiency.

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, COVID-19 and other biological contexts.

This Resource presents the Quantum Optimization Benchmarking Library, which enables fair, reproducible benchmarks of quantum heuristics for ten difficult combinatorial optimization classes with baseline results to track progress towards quantum advantage.

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 supporting validation on a superconducting quantum processor.

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 data, improving discovery in large biobank studies.

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, revealing income segregation as a core determinant of human flows.

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.

We discuss common feedback from reviewers based on our experience as editors.

Gene regulatory networks provide a systems-level view of transcriptional control. Advances in biotechnology and computational modeling are reshaping gene regulatory network inference and opening up opportunities for mechanistic insight.