Generative deep learning for macromolecular structure and dynamics

Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function. A highly visible instance of this is in molecular biology, where characterizing macromolecular structure and dynamics is central to a detailed, molecular-level understanding of biological processes in the living cell. The current computational paradigm utilizes optimization as the generative process for modeling both structure and structural dynamics. Computational biology researchers are now attempting to wield generative models employing deep neural networks as an alternative computational paradigm. In this review, we summarize such efforts. We highlight progress and shortcomings. More importantly, we expose challenges that macromolecular structure poses to deep generative models and take this opportunity to introduce the structural biology community to several recent advances in the deep learning community that promise a way forward.

Automated summary

Scientific enquiries often rely on a mechanistic treatment of dynamic systems, linking form to function. Molecular biology emphasizes characterizing macromolecular structure and dynamics for a better understanding of biological processes in living cells. Computational biology researchers are now exploring deep neural networks as an alternative computational paradigm for generative models.