Nature Machine Intelligence, Published online: 25 June 2026; doi:10.1038/s42256-026-01258-0

Rising pathogen drug resistance makes next-generation antimicrobial peptides a global priority. Generative AI accelerates discovery by rapidly proposing new peptides with high therapeutic potential. The key question is no longer whether broad data-driven exploration is possible, but whether it can refine biologically complex activity scaffolds.

Nature Machine Intelligence, Published online: 23 June 2026; doi:10.1038/s42256-026-01269-x

Recent breakthroughs in mathematical research show that AI is transforming the field at a remarkable pace. In an open letter published this month, an international group of mathematicians argue that the field needs to remain a human endeavour.

Nature Machine Intelligence, Published online: 23 June 2026; doi:10.1038/s42256-026-01263-3

Nassour, Berberich and colleagues present a soft robotic hand exoskeleton that restores grasping ability in individuals with severe hand paralysis, enabling meaningful tasks such as feeding. A lightweight textile glove with wrist dorsiflexion and an active opposable thumb increases hand articulations to enable more dexterous grasping.

Nature Machine Intelligence, Published online: 22 June 2026; doi:10.1038/s42256-026-01252-6

An, Luo, Zhang and colleagues present Turbo, a transformer-based reinforcement learning framework that enables simulation-to-real transfer for autonomous navigation and obstacle avoidance in physical microrobotic swarms operating in unknown environments.

Nature Machine Intelligence, Published online: 16 June 2026; doi:10.1038/s42256-026-01255-3

Pengfei Sun et al. develop a spiking neural network with a dual memory pathway, co-designed with a custom neuromorphic chip. The approach delivers over 4× throughput and 5x energy efficiency gains while using 40–60% fewer parameters than state-of-the-art implementations.

Nature Machine Intelligence, Published online: 12 June 2026; doi:10.1038/s42256-026-01256-2

Psychiatric disorders are heterogeneous, and care depends on interpreting unstructured longitudinal narratives, creating variability that hinders standardization. A study now shows that a psychiatry-specific large language model (LLM) may help clinicians to deliver more consistent, high-quality care.

Nature Machine Intelligence, Published online: 11 June 2026; doi:10.1038/s42256-026-01244-6

Synthetic datasets are becoming crucial for the development of biomedical machine learning models. Victoriano et al. discuss the persistent simulation-to-reality gap that limits how well synthetic performance predicts real-world performance.

Nature Machine Intelligence, Published online: 11 June 2026; doi:10.1038/s42256-026-01250-8

Xiong et al. introduce ConfSeq, a molecular conformation description language that enables language models to perform three-dimensional molecular modelling tasks, including conformer prediction, three-dimensional molecular generation and representation, with strong performance.