# Generating protein sequences from antibiotic resistance genes data using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1904.13240 · HTML: https://mlsanity.com/paper/1904.13240/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2019.

## Built on by (most cited first, 2 of 2)

- [xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein](https://mlsanity.com/paper/2401.06199/) (2024)
- [Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization](https://mlsanity.com/paper/2209.06259/) (2022)

## Abstract (arXiv)

We introduce a method to generate synthetic protein sequences which are predicted to be resistant to certain antibiotics. We did this using 6,023 genes that were predicted to be resistant to antibiotics in the intestinal region of the human gut and were fed as input to a Wasserstein generative adversarial network (W-GAN) model a variant to the original generative adversarial model which has been known to perform efficiently when it comes to mimicking the distribution of the real data in order to generate new data which is similar in style to the original data which was fed as the training data