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Focus on computational psychiatry

Our October issue is now live, and it includes a Focus that examines the advances in computational psychiatry and the challenges of deploying computational models to alleviate mental health disorders.

Announcements

  • A conceptual illustration of mathematics, with equations floating around.

    In this cross-journal Collection, we aim to bring together research on physics-informed machine learning, which uses prior available knowledge in the form of physical laws and equations to improve the training of machine learning models, making these predictive models potentially more efficient, robust, and trustworthy.

    Open for submissions
  • A molecular structure with particles on color gradient background.

    Generative models have gained widespread attention in recent years due to their inverse design capabilities and their potential to accelerate the molecular design and discovery processes. This Collection includes manuscripts published by Nature Computational Science that apply and develop generative modeling tools for small molecule design and discovery.

  • Aerial view of a crowd connected by lines.

    The use of computational methods and tools to deepen our understanding of long-standing questions in the social sciences has been rapidly growing in recent years. This Collection includes manuscripts published by Nature Computational Science – from research papers to Review articles and opinion pieces – that are relevant to computational social science.

  • A multi-color group of people encircled by charts and data

    The year 2023 marks the mid-point of the 15-year period envisaged to achieve the Sustainable Development Goals. In this Nature Portfolio Collection, you will find studies across different journals that assess progress or that showcase interventions that have made a difference. We also welcome submissions of studies framed in a similar way.

    Open for submissions

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  • 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.

    Editorial
  • 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.

    • Quentin J. M. Huys
    • Michael Browning
    Comment
  • 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.

    • Evan Collins
    • Robert Langer
    • Daniel G. Anderson
    Comment
  • 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.

    Editorial
  • 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.

    • Nathan Leroux
    • Jan Finkbeiner
    • Emre Neftci
    Comment