Reviews & Analysis

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  • Antibodies are an essential class of therapeutics but low breadth or off-target binding are major concerns for antibody–drug efficiency and safety. To predict which targets an antibody can neutralize, a machine learning pipeline based on an adaptive graph convolutional network architecture is proposed that learns the binding landscape of antibodies to multiple mutated viruses at the same time.

    • Philippe A. Robert
    • Victor Greiff
    News & Views
  • Finding good benchmarks is an important and pervasive problem in machine learning for healthcare. This Perspective highlights key aspects that require scrutiny in the whole process of benchmark generation and use, including problem formulation, creation of datasets, development of a suite of machine learning models and evaluation of these models.

    • Diana Mincu
    • Subhrajit Roy
  • The metaverse is gaining prominence in industry, academia and social media. Wang and colleagues envision a medical technology and AI ecosystem, and present this perspective on the future of healthcare in the metaverse.

    • Ge Wang
    • Andreu Badal
    • Rongping Zeng
  • Tool use is one of the defining traits of human cognition that sets our species apart from other animals. A novel computational framework may enable robots to use tools as intelligently as humans do.

    • Lorenzo Jamone
    News & Views
  • In animals, both body and neural control have co-evolved to be adaptable to the environment. While a newborn foal learns quickly how to use its legs, traditional robotic approaches require careful engineering and calibration for stable walking robots. Bio-inspired robotics aims to bridge this gap.

    • Francisco J. Valero-Cuevas
    • Andrew Erwin
    News & Views
  • It has become rapidly clear in the past few years that the creation, use and maintenance of high-quality annotated datasets for robust and reliable AI applications requires careful attention. This Perspective discusses challenges, considerations and best practices for various stages in the data-to-AI pipeline, to encourage a more data-centric approach.

    • Weixin Liang
    • Girmaw Abebe Tadesse
    • James Zou
  • Designing viable molecular candidates is pivotal to devising low-cost and sustainable storage systems. A reinforcement learning framework has been developed that can identify stable candidates for redox flow batteries in the large search space of organic radicals.

    • Yang Cao
    • Cher Tian Ser
    • Alán Aspuru-Guzik
    News & Views
  • Directed, active transport of cargo is essential for life on all length scales. A new system of artificial microtubules — consisting of a fibre with an embedded periodic array of magnetic inclusions — provides controlled active transport of microcargo by a rotating magnetic field, even under adverse flow conditions.

    • Gerhard Gompper
    News & Views
  • Deep learning models for sequential data can be trained to make accurate predictions from large biological datasets. New tools from computer vision and natural language processing can help us make these models interpretable to biologists.

    • Ahmed M. Alaa
    News & Views
  • Microscopy-based drug screens with fluorescent markers can shed light on how drugs affect biological processes. Without adding markers and imaging channels, which is cumbersome and costly, a new generative deep-learning method extracts new fluorescence channels from images, potentially improving the drug-discovery pipeline.

    • Florian Heigwer
    News & Views
  • Neural networks can be implemented by using purified DNA molecules that interact in a test tube. Convolutional neural networks to classify high-dimensional data have now been realized in vitro, in one of the most complex demonstrations of molecular programming so far.

    • William Poole
    News & Views
  • Unsupervised machine learning algorithms reduce the dependence on curated, labeled datasets that are characteristic of supervised machine learning. The authors argue that the developmental science of infant cognition could inform the design of unsupervised machine learning approaches.

    • Lorijn Zaadnoordijk
    • Tarek R. Besold
    • Rhodri Cusack
  • Both proteins and natural language are essentially based on a sequential code, but feature complex interactions at multiple scales, which can be useful when transferring machine learning models from one domain to another. In this Review, Ferruz and Höcker summarize recent advances in language models, such as transformers, and their application to protein design.

    • Noelia Ferruz
    • Birte Höcker
    Review Article
  • Despite the growing number of initiatives that employ AI to counter corruption, few studies empirically tackle the political and social consequences of embedding AI in anti-corruption efforts. The authors outline the societal and technical challenges that need to be overcome for AI to fight corruption.

    • Nils Köbis
    • Christopher Starke
    • Iyad Rahwan
  • Machine reading and knowledge extraction methods can be used to mine the scientific literature and reveal the direction and robustness of discoveries. Such efforts now point to the importance of independent tests of reported claims.

    • Luís A. Nunes Amaral
    News & Views
  • It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines.

    • Dhireesha Kudithipudi
    • Mario Aguilar-Simon
    • Hava Siegelmann
  • GPUs, which are highly parallel computer processing units, were originally designed for graphics applications, but they have played an important role in accelerating the development of deep learning methods. In this Review, Pandey and colleagues summarize how GPUs have advanced machine learning in the field of drug discovery.

    • Mohit Pandey
    • Michael Fernandez
    • Artem Cherkasov
    Review Article
  • Graph convolutional networks have become a popular tool for learning with graphs and networks. We reflect on the reasons behind the success story.

    • Mostafa Haghir Chehreghani
    News & Views