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.

Computational psychiatry entails applying data-driven methods and artificial intelligence to model the mechanisms underlying mental health diseases, offering ...

Integrating computational methods with brain-based data presents a path to precision psychiatry by capturing individual neurobiological variation, improving diagnosis, prognosis, and personalized care. This Viewpoint highlights advances in normative and foundation models, the importance of clinically grounded principles, and the role of robust measurement and interpretability in progressing mental health care.

In this Perspective, the authors examine privacy risks in mental health AI, and explore solutions and evaluation frameworks to balance privacy–utility trade-offs. They suggest a pipeline for developing privacy-aware mental health AI systems.

Large language models (LLMs) offer promising ways to enhance psychotherapy through greater accessibility, personalization and engagement. This Perspective introduces a typology that categorizes the roles of LLMs in psychotherapy along two critical dimensions: autonomy and emotional engagement.

In order to realize the potential of mental health AI applications to deliver improved care, a multipronged approach is needed, including representative AI datasets, research practices that reflect and anticipate potential sources of bias, stakeholder engagement, and equitable design practices.

Computational psychiatry is increasingly delivering causal evidence by focusing on interventions research and clinical trials. Causal evidence could improve patient outcomes through improved precision, repurposing, novel interventions, scaling of psychotherapy and better translation to the clinic.

Nature Computational Science presents a Focus that explores the field of computational psychiatry and its key challenges, from privacy concerns to the ethical use of artificial intelligence, offering new insights into the future of mental health care.

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.