Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization.

ABSTRACT: Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-stan

SUBMITTER: Durrant JD 

PROVIDER: S-EPMC9512884 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature