A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences.

ABSTRACT: Engineering gene and protein sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence design. The generated sequences can however get stuck in local minima and often have low diversity. Here, we develop deep exploration networks (DENs), a class of activation-maximizing generative models, which minimize the cost of a neural network fitness predictor by gradient descent. By penalizing any two generated patterns on the basis of a similarity metric, DENs explicitly maximize sequence diversity. To avoid drifting into low-confidence regions of the predictor, we incorporate variational autoencoders to maintain the likelihood ratio of generated sequences. Using DENs, we

SUBMITTER: Linder J 

PROVIDER: S-EPMC8694568 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature