Nature Machine Intelligence, Published online: 10 September 2026; doi:10.1038/s42256-026-01298-6

Shi et al. demonstrate a dual architecture approach for materials research, integrating two lightweight large language models for collaborative reasoning and scientific tool execution. The method achieves competitive performance while remaining affordable and locally deployable.

Nature Machine Intelligence, Published online: 19 August 2026; doi:10.1038/s42256-026-01291-z

Chen, Wang, Li et al. introduce VITAL, a dual-channel deep learning framework that co-learns sequence and structural contexts to quantitatively predict peptide–protein interactions, map binding interfaces and estimate affinity.

Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01264-2

Liu and colleagues introduce HelixFold-S1, a guided sampling strategy for biomolecular complex structure prediction that targets high-probability interaction regions. The method achieves higher accuracy than traditional unguided methods while reducing computational costs.