This study reports a machine learning decoder that efficiently corrects errors in quantum logical circuits with entangling gates. The Multi-Core Circuit Decoder achieves competitive accuracy while run...

This study introduces a data-driven method for ranking law firms based on litigation outcomes, revealing that traditional reputation-based rankings do not reflect legal performance accurately.

In this study the authors present SWITCH, a deep learning model that integrates unpaired spatial multi-omics data and enables unsupervised cross-modal prediction, aiding spatial domain identification ...

This study explores the use of quantum computing to address multi-objective optimization challenges. By using a low-depth quantum approximate optimization algorithm to approximate the optimal Pareto f...

This study presents a neural symbolic regression approach that autonomously uncovers network dynamics from data. It was demonstrated to refine existing models of gene regulation and ecology, and ident...

Investigating crystalline materials often requires calculations for many variations of a system, substantially increasing the computational burden. By training a transferable neural wavefunction acros...

A large perturbation model that integrates diverse laboratory experiments is presented to predict biological responses to chemical or genetic perturbations and support various biological discovery tas...

This study presents ECloudGen, which uses latent diffusion to generate electron clouds from protein pockets and decodes them into molecules. The adopted two-stage training expands the chemical space a...

This study presents a flexible AI-based method for compressing microscopy images, achieving high compression while preserving details critical for analysis, with support for task-specific optimization and arbitrary-resolution decompression.

The 2025 Nobel Prize in Chemistry has been awarded to Susumu Kitagawa, Richard Robson and Omar M. Yaghi “for the development of metal–organic frameworks.”

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.

An algorithm for proteoform identification with top-down mass spectra is proposed, and a pipeline is developed for generating simulated top-down spectra on the basis of input protein sequences with modifications.

This work develops CRISP, a framework using foundation models to predict drug responses in previously unseen cell types at single-cell resolution, advancing drug repurposing and drug screening capabilities.

Self-driving laboratories that integrate robotic production with artificial intelligence have the potential to accelerate innovation in biotechnology. Because self-driving labs can be complex and not universally applicable, it is useful to consider their suitable use cases for successful integration into discovery workflows. Here, we review strategies for assessing the suitability of self-driving labs for biochemical design problems.

EMO integrates DNA sequence and chromatin accessibility data to predict how noncoding variants regulate gene expression across tissues and single cells, enabling context-aware personalized insights into genetic effects for precision medicine.

This study presents a brain rhythm-based inference model (BRyBI) for speech processing in the auditory cortex. BRyBI shows how rhythmic neural activity enables robust speech processing by dynamically predicting context and elucidates mechanistic principles that allow robust speech parsing in the brain.

Strong barriers remain between neuromorphic engineering and machine learning, especially with regard to recent large language models (LLMs) and transformers. This Comment makes the case that neuromorp...

This Perspective discusses that generative AI aligns with generative linguistics by showing that neural language models (NLMs) are formal generative models. Furthermore, generative linguistics offers ...

Larger LLMs’ self-attention more accurately predicts readers’ regressive saccades and fMRI responses in language regions, whereas instruction tuning adds no benefit.

Large language models remain largely unexplored is the design of cities. In this Perspective, the authors discuss the potential opportunities brought by these models in assisting urban planning.

The authors report a memristor-based system that analyzes raw analog signals from a genomic sequencer directly in memory. By bypassing slow data conversion, the system achieves substantial improvement...

Secure collaborative genome-wide association studies (GWAS) with population-scale datasets address gaps in genomic data. This work proposes SECRET-GWAS and system optimizations that overcome resource ...