Conditional generative modeling forprotein design with hierarchical functions

Tim Kucera, Matteo Togninalli, Laetitia Meng-Papaxanthos

Abstract: 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.