Announcements

  • The multiple disciplines (including biological sciences, physical sciences, and environmental sciences) that are covered by Nature Computational Science.

    Check out our one-year anniversary collection, in which we highlight some of the research articles, published during our first year, that reported stimulating ideas, methods and results in many different science areas, including biological sciences, physical sciences, and environmental sciences.

Nature Computational Science 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.

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    • The identification of robust and generalizable biomarkers based on microbial abundance data is a challenging task. An algorithm shows an enhanced classification performance by quantifying shifts in microbial co-abundances.

      • Leo Lahti
      News & Views
    • Variational Monte Carlo is one of the most accurate methods to solve the many-electron Schrödinger equation, but suffers from high computational cost. A recent study uses a weight-sharing technique to accelerate the neural network-based variational Monte Carlo method, allowing accurate and effective simulations of molecules.

      • Huan Tran
      News & Views
    • Determining the origin of engineered DNA can help to foster responsible innovation within the biotechnology community. A convolutional neural network approach that learns distances between engineered DNA sequences and various labs that could have created them is used to accurately predict the lab-of-origin.

      Research Briefing
    • A dynamic model of SARS-CoV-2 transmission is integrated with a 63-sector economic model to identify control strategies for optimizing economic production while keeping schools and universities operational, and for constraining infections such that emergency hospital capacity is not exceeded.

      • Aditya Goenka
      • Lin Liu
      News & Views
    • Biomimetic nanoparticles can form complexes with proteins. Structural descriptors have been identified to predict nanoparticle–protein complex formation and their interaction sites. These descriptors include geometrical and graph-theoretical molecular features that are universally applicable to all nanoscale macromolecules of both organic and inorganic chemistries.

      Research Briefing