Reviews & Analysis

As digital data expand exponentially, traditional storage media are becoming less viable, making DNA a promising solution due to its density and durability. In this Perspective, the authors discuss the critical computational challenges associated with in vitro DNA-based data storage.

The application of machine learning techniques to small-molecule drug discovery has not yet yielded a true leap forward in the field. This Perspective discusses how a renewed focus on data and validation could help unlock machine learning’s potential.

Mechanical metamaterials have shown potential for processing information via autonomous environmental interactions. This Perspective summarizes recent efforts and challenges on integrating stimuli-responsive materials with mechanical metamaterials for mechanical computing, and explores the remaining challenges in the field.

Computational tools have recently empowered mechanical metamaterials design. In this Perspective, advances to these approaches are discussed, notably mechanism-based design, topology optimization, the use of machine learning and the challenges for additive-manufactured metamaterial structures.

Brain-age estimation is gaining attention as a biomarker for brain health as it provides a unique perspective on aging. This Perspective reviews current advancements and future directions to ensure deployment in hospital settings.

While large-scale GPS location datasets have been instrumental to applications in epidemiology, there are still several challenges with these data that should be considered and addressed to make data-driven epidemiology more reliable.

The parallels between natural language and antibody sequences could serve as a stepping stone to using deep language models for analyzing antibody sequences. This Perspective discusses how issues in antibody language model rule mining could be addressed by linguistically formalizing the antibody language.

Discovering improved semiconductor materials is essential for optimal device fabrication. In this Perspective, data-driven computational frameworks for semiconductor discovery and device development are discussed, including the challenges and opportunities moving forward.

While there is a clear opportunity for digital twins to bring value in mechanical and aerospace engineering, they must be considered as an asset in their own right so that their full potential can be realized.

The application of digital twins in industry has become increasingly common, but not without important challenges to be addressed by the research community.

Although digital twins first originated as models of physical systems, they are rapidly being applied to social systems, such as cities. This Perspective discusses the development and use of digital twins for urban planning.

The digital twin concept, while initially formulated and developed in industry and engineering, has compelling potential applications in medicine. There are, however, major challenges that need to be overcome to fully embrace digital twin technology in the medical context.

As computation is increasingly integrated into drug research and development, this Perspective analyzes company business models, funding and deals to provide unique insights into risks and opportunities in this quickly maturing industry, which aims to expedite the creation of life-saving therapeutics.

Language models offer promises in encoding quantum correlations and learning complex quantum states. This Perspective discusses the advantages of employing language models in quantum simulation, explores recent model developments, and offers insights into opportunities for realizing scalable and accurate quantum simulation.