Reviews: Generative Models for Graph-Based Protein Design

This paper addresses the problem of generation of protein sequences for a desired 3D structure, also known as the "inverse protein folding problem". The authors introduce a model inspired by recent advances in language modeling (for the sequence decoder part of the model) and graph representation learning (for the encoder part of the model). Protein structures are represented as k-NN graphs, enriched with orientation/location-based features and features based on structural bindings. The encoder takes the form of an adapted Graph Attention Network, here termed "Structured Transformer", which is enriched with edge features and relative positional encodings, and the decoder takes the form of an auto-regressive Transformer-based model. Results indicate improvements over a recent deep neural network baseline for this task.