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.

The authors introduce a method that combines physics and machine learning to design dynamic unstructured proteins with tunable ensemble properties like size, shape, sensing and binding.

A benchmark to evaluate spatial alignment methods for spatial transcriptomics shows that the best approach is dataset dependent and offers actionable guidance for the future development of methods.

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.

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.

HELIX is a deep learning framework for modeling tissue- and context-specific RNA splicing and isoform usage, prioritizing splicing-altering genetic variants and their functional effects from bulk and single-cell RNA sequencing data.

This work reports HESpotEx, an artificial intelligence model that predicts spot-level spatial gene expression from whole-slide histopathology images to capture the tissue molecular patterns across cancers and inflammatory diseases.

A general-purpose large language model can be trained to create a specialized model, SmileyLlama, that can generate drug-like molecules with user-specified properties that are relevant to medicinal chemistry through a process that might be extended to other scientific domains.

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.

The authors propose a method that unifies finite element methods and machine learning by using neural operators as elements to model complex subdomains, yielding an efficient and scalable numerical framework with highly reusable machine learning models.

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.

After years of progress, density functional theory is entering a period of rapid advancement, enabled by emerging generalized schemes, richer descriptors, machine learning, and the anticipated development of broader, higher-quality datasets.

Submitting an appeal regarding an editorial decision may require a significant investment of time and effort from authors. Therefore, it is important to understand what an appeal entails before making the decision on whether to appeal.