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

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