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.

应用生成对抗网络（GAN）生成编码变长蛋白质的合成DNA序列，并提出了一种名为Feedback GAN（FBGAN）的新型反馈回路体系结构来优化合成基因序列。通过基因序列针对所需性质的外部函数分析器进行优化，并用于生成抗菌肽编码的合成基因和优化编码的合成基因的辅助结构的示例。

DNA的反馈GAN（FBGAN）：一种用于优化蛋白质功能的新型反馈循环结构