February 24 Issue

February issue now live

Qiao, Z., Nie, W., Vahdat, A. et al. State-specific protein–ligand complex structure prediction with a multiscale deep generative model.

Nature Machine Intelligence is a Transformative Journal; authors can publish using the traditional publishing route OR via immediate gold Open Access.

Our Open Access option complies with funder and institutional requirements.

Advertisement

  • Foundation models have transformed artificial intelligence by training on vast amounts of broad unlabelled data. Pai et al. present a foundation model leading to more accurate, efficient and robust cancer imaging biomarkers, especially in use cases with small training datasets.

    • Suraj Pai
    • Dennis Bontempi
    • Hugo J. W. L. Aerts
    ArticleOpen Access
  • Deep learning generative approaches have been used in recent years to discover new molecules with drug-like properties. To improve the performance of such approaches, Yang et al. add chemical binding knowledge to a deep generative framework and demonstrate, including by wet-lab verification, that the method can find valid molecules that successfully bind to target proteins.

    • Yuanyuan Jiang
    • Guo Zhang
    • Shengyong Yang
    Article
  • This Reusability Report examines a recently published deep learning method PENCIL by Ren et al. for identifying phenotype populations in single-cell data. Cao et al. reproduce here the main results, analyse the sensitivity of the method to model parameters and describe how the method can be used to create a signature for immunotherapy response markers.

    • Yingying Cao
    • Tian-Gen Chang
    • Eytan Ruppin
    Article
  • Although federated learning is often seen as a promising solution to allow AI innovation while addressing privacy concerns, we argue that this technology does not fix all underlying data ethics concerns. Benefiting from federated learning in digital health requires acknowledgement of its limitations.

    • Marieke Bak
    • Vince I. Madai
    • Stuart McLennan
    Comment
  • Can non-state multinational tech companies counteract the potential democratic deficit in the emerging global governance of AI? We argue that although they may strengthen core values of democracy such as accountability and transparency, they currently lack the right kind of authority to democratize global AI governance.

    • Eva Erman
    • Markus Furendal
    Comment
  • One of the most successful areas for deep learning in scientific discovery has been protein predictions and engineering. We take a closer look at four studies in this issue that advance protein science with innovative deep learning approaches.

    Editorial
  • We reflect on five years of Nature Machine Intelligence and on providing a venue for discussions in AI.

    Editorial
  • For our fifth anniversary, we reconnected with authors of recent Comments and Perspectives in Nature Machine Intelligence and asked them how the topic they wrote about developed. We also wanted to know what other topics in AI they found exciting, surprising or worrying, and what their hopes and expectations are for AI in 2024—and the next five years. A recurring theme is the ongoing developments in large language models and generative AI, their transformative effect on the scientific process and concerns about ethical implications.

    • Noelia Ferruz
    • Marinka Zitnik
    • Francesco Stella
    Feature