PandoraGAN: Generating antiviral peptides using Generative Adversarial Network

Surana, Shraddha; Arora, Pooja; Singh, Divye; Sahasrabuddhe, Deepti; Valadi, Jayaraman

Motivation The continuous increase in pathogenic viruses and the intensive laboratory research for development of novel antiviral therapies often poses challenge in terms of cost and time efficient drug design. This accelerates research for alternate drug candidates and contributes to recent rise in research of antiviral peptides against many of the viruses. With limited information regarding these peptides and their activity, modifying the existing peptide backbone or developing a novel peptide is very time consuming and a tedious process. Advanced deep learning approaches such as generative adversarial networks (GAN) can be helpful for wet lab scientist to screen potential antiviral candidates of interest and expedite the initial stage of peptide drug development. To our knowledge this is the first ever use of GAN models for antiviral peptides across the viral spectrum. Results In this study, we develop PandoraGAN that utilizes GAN to design bio active antiviral peptides. Available antiviral peptide data was manually curated for preparing highly active peptides data set to include peptides with lower IC50 values. We further validated the generated sequences comparing the physico-chemical properties of generated antiviral peptides with manually curated highly active training data. Availability Antiviral sequences generated by PandoraGAN are available on PandoraGAN server. https://pandora-gan.herokuapp.com/. The code is available at https://gitlab.com/shraddha.surana/antiviral-peptide-predictions-using-gan Contact