  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
