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.”

Rapid identification of pathogenic viruses remains a critical challenge. A recent study advances this frontier by demonstrating a fully integrated memristor-based hardware system that accelerates genomic analysis by a factor of 51, while reducing energy consumption to just 0.2% of that required by conventional computational methods.

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.

Large language models (LLMs) are rapidly being implemented in a wide range of disciplines, with the promise of unlocking new possibilities for scientific ...

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 neuromorphic engineering may hold the keys to more efficient inference with transformer-like models.

While leading tech companies race to build ever-larger models, researchers in Brazil, India and Africa are using clever tricks to remix big labs’ LLMs to bring AI to billions of users.

The use of generative artificial intelligence (AI) in healthcare is advancing, but understanding its potential challenges for fairness and health equity is still in its early stages. This Comment investigates how to define fairness and measure it, and highlights research that can help address challenges in the field.

Large language models are increasingly important in social science research. The authors provide guidance on how best to validate and use these models as rigorous tools to further scientific inference.

This Perspective highlights the potential integrations of large language models (LLMs) in chemical research and provides guidance on the effective use of LLMs as research partners, noting the ethical and performance-based challenges that must be addressed moving forward.

Many humanists are skeptical of language models and concerned about their effects on universities. However, researchers with a background in the humanities are also actively engaging with artificial intelligence — seeking not only to adopt language models as tools, but to steer them toward a more flexible, contextual representation of written culture.

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.

Large language models (LLMs) are already transforming the study of individual cognition, but their application to studying collective cognition has been underexplored. We lay out how LLMs may be able to address the complexity that has hindered the study of collectives and raise possible risks that warrant new methods.

The adoption of generative artificial intelligence (AI) code assistants in scientific software development is promising, but user studies across an array of programming contexts suggest that programmers are at risk of over-reliance on these tools, leading them to accept undetected errors in generated code. Scientific software may be particularly vulnerable to such errors because most research code is untested and scientists are undertrained in software development skills. This Comment outlines the factors that place scientific code at risk and suggests directions for research groups, educators, publishers and funders to counter these liabilities.

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 a framework for evaluating and improving NLMs.

This issue of Nature Computational Science features a Focus that highlights both the promises and perils of large language models, their emerging applications across diverse scientific domains, and the opportunities to overcome the challenges that lie ahead.

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.