# Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions

arXiv: https://arxiv.org/abs/1804.01694 · HTML: https://mlsanity.com/paper/1804.01694/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC](https://mlsanity.com/paper/2212.09925/) (2022)
- [Cliqueformer: Model-Based Optimization with Structured Transformers](https://mlsanity.com/paper/2410.13106/) (2024)
- [Reinforcement Learning for Sequence Design Leveraging Protein Language Models](https://mlsanity.com/paper/2407.03154/) (2024)
- [Property-Isometric Variational Autoencoders for Sequence Modeling and Design](https://mlsanity.com/paper/2509.14287/) (2025)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) represent an attractive and novel approach to generate realistic data, such as genes, proteins, or drugs, in synthetic biology. Here, we apply GANs to generate synthetic DNA sequences encoding for proteins of variable length. We propose a novel feedback-loop architecture, called Feedback GAN (FBGAN), to optimize the synthetic gene sequences for desired properties using an external function analyzer. The proposed architecture also has the advantage that the analyzer need not be differentiable. We apply the feedback-loop mechanism to two examples: 1) generating synthetic genes coding for antimicrobial peptides, and 2) optimizing synthetic genes for the secondary structure of their resulting peptides. A suite of metrics demonstrate that the GAN generated proteins have desirable biophysical properties. The FBGAN architecture can also be used to optimize GAN-generated datapoints for useful properties in domains beyond genomics.