Efficient generative modeling of protein sequences using simple autoregressive models

Abstract

Deep learning is a powerful tool for the design of novel protein sequences, yet can be computationally very inefficient. Here the authors propose using simple forecasting models to efficiently generate a large number of novel protein structures.