Generative models for graph-based protein design

John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola

Research output: Contribution to conferencePaperScientificpeer-review

10 Citations (Scopus)


Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, and the task of finding a viable design is often referred to as the inverse protein folding problem. We develop generative models for protein sequences conditioned on a graph-structured specification of the design target. Our approach efficiently captures the complex dependencies in proteins by focusing on those that are long-range in sequence but local in 3D space. Our framework significantly improves upon prior parametric models of protein sequences given structure, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.

Original languageEnglish
Publication statusPublished - 2019
MoE publication typeNot Eligible
EventDeep Generative Models for Highly Structured Data - New Orleans, United States
Duration: 6 May 20196 May 2019


WorkshopDeep Generative Models for Highly Structured Data
Abbreviated titleDGS@ICLR Workshop
Country/TerritoryUnited States
CityNew Orleans
OtherDGS@ICLR Workshop


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