Nature Machine Intelligence, Published online: 10 June 2026; doi:10.1038/s42256-026-01257-1

Based on independent reproduction and external benchmarks, this study shows that RXNGraphormer is reproducible and transferable across reaction prediction tasks, while revealing limitations under heterogeneous and distribution-shifted settings.

Nature Machine Intelligence, Published online: 09 June 2026; doi:10.1038/s42256-026-01251-7

Machine learning methods for drug discovery often face difficulties in identifying novel bioactive molecules that do not belong to the training data distribution. A new metric can now quantify chemical distribution shift and evaluate the generalization capability of molecular machine learning models.

Nature Machine Intelligence, Published online: 04 June 2026; doi:10.1038/s42256-026-01249-1

Yang et al. developed CrossDNA, a parameter-efficient language model for cross-strand DNA modelling inspired by double-strand dynamics. It performs strongly in benchmarks and supports regulatory region interpretation and non-coding variant prioritization.

Nature Machine Intelligence, Published online: 01 June 2026; doi:10.1038/s42256-026-01242-8

Driessen et al. present a conditional optimal transport method that can model the distribution shift between perturbed and unperturbed cell transcriptomes and that can generalize to unseen contexts.

Nature Machine Intelligence, Published online: 01 June 2026; doi:10.1038/s42256-026-01245-5

Yuan et al. develop UniCM, a deep learning framework that models the coupled dynamics of global climate modes as an interconnected system, extending skilful forecasts for multiple climate modes and revealing cross-basin interactions.