Bar-Lev et al. propose a high-efficiency DNA-based storage pipeline that integrates deep neural networks, error-correcting codes and safety margins, achieving a 3,200× speed improvement and a 40% accuracy gain, paving the way for commercially viable DNA data storage.

It is challenging to compare how well robots perform a task, as the evaluation depends on the process and skills required. It is proposed to group robots into a taxonomy based on their performance on a set of embodied skill benchmarks.

To enable artificial agents to generate human-like goals, a model must capture the complexity and diversity of human goals. Davidson et al. model playful goals from a naturalistic experiment as reward-producing programs, mapping an agent’s behaviour to goal success. They then develop a computational model to generate diverse human-like goals.

Chen et al. present a deep learning-based lead optimization model that combines generative artificial intelligence with structure-based approaches. The method is successfully applied to the design of drug-like molecules targeting the recently identified LTK protein target with high potency and selectivity.

Large language models are being considered to simulate responses from participants of different backgrounds in computational social science experiments. Here it is shown that this practice can misportray and flatten demographic groups in distinctively harmful ways.

SketchMol is a model that explores the feasibility of incorporating image generation techniques into the field of small-molecule design.

The authors address the challenge of predicting drug–target interactions, which is crucial for drug repurposing, by introducing a robust benchmarking framework. Using a biologically driven strategy, they uncover previously unknown interactions.

Artificial intelligence (AI)-based docking and scoring methods demonstrate considerable potential for virtual drug screening. Gu et al. go further by assessing the structural rationality of AI-predicted complex conformations from various sources.

Cha and colleagues present a translation- and rotation-equivariant autoencoder-based method for robust image recognition, which they demonstrate on diverse tasks from bioinformatics, material science and astronomy.

Humans continuously acquire knowledge and develop complex behaviours. Meng, Bing, Yao and colleagues present a robotic lifelong learning framework using a Bayesian non-parametric knowledge space, enabling agents to dynamically preserve and integrate knowledge from sequential tasks, enhancing adaptability.

Accurate prediction of immunogenic CD8⁺ T cell epitopes would greatly accelerate T cell vaccine development. A new deep learning model, MUNIS, can rapidly identify HLA-binding, immunogenic and immunodominant peptides in foreign pathogens.

This work proposes a deep learning model based on the cross-attention mechanism to simultaneously predict peptide–HLA and peptide–TCR bindings. Experiments verify that its performance for both prediction tasks on multiple test sets compares favourably with previous methods.

Akiba et al. developed an evolutionary approach to automatically merge artificial intelligence models, creating powerful hybrid models without extensive training. The method produces models with enhanced mathematical and visual capabilities that outperform larger models.

Integrating incomplete multi-omics data remains a key challenge in precision oncology. IntegrAO, an unsupervised framework that integrates diverse omics, enables accurate patient classification even with incomplete datasets.

Understanding how people perceive and interpret uncertainty from large language models (LLMs) is crucial, as users often overestimate LLM accuracy, especially with default explanations. Steyvers et al. show that aligning LLM explanations with their internal confidence improves user perception.

Large language models promise substantial advances in molecular modelling and design. A multimodal benchmark is proposed to analyse performance, and 1,263 experiments are conducted to examine the compatibility of a large language model with data modalities and knowledge acquisition.

A unified evolution-driven deep learning framework is presented, which outperforms state-of-the-art methods across various virus mutational driver predictions, and which captures fundamental patterns of virus evolution.

COMET, an artificial intelligence method that improves the analysis of small medical studies using large clinical databases, has been created. COMET can help develop better artificial intelligence tools and identify key biomarkers across many diseases, potentially changing medical research.

Zhang and colleagues introduce an inter-cell learning mechanism to predict battery lifetime in the presence of diverse ageing conditions.

Batatia and colleagues introduce a computational framework that combines message-passing networks with the atomic cluster expansion architecture and incorporates a many-body description of the geometry of molecular structures. The resulting models are interpretable and accurate.