Conditional generative modeling for de novo protein design with hierarchical functions.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Protein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computational or experimental protocols. At the same time, machine learning has enabled the solving of complex problems by leveraging large amounts of available data, more recently with great improvements on the domain of generative modeling. Yet, generative models have mainly been applied to specific sub-problems of protein design.

Authors

  • Tim Kucera
    Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland.
  • Matteo Togninalli
    Visium, Lausanne 1015, Switzerland.
  • Laetitia Meng-Papaxanthos
    Google Research, Brain Team, Zurich 8002, Switzerland.