GANDALF

Abstract

Pharmaceutical drug design is a difficult and costly endeavor. Computational drug design has the potential to help save time and money by providing a better starting point for new drugs with an initial computational evaluation completed. We propose a new application of Generative Adversarial Networks (GANs), called GANDALF (Generative Adversarial Network Drug-tArget Ligand Fructifier) to design new peptides for protein targets. Other GAN based methods for computational drug design can only generate small molecules, not peptides. It also incorporates data such as active atoms, not used in other methods, which allow us to precisely identify where interaction occurs between a protein and ligand. Our method goes farther than comparable methods by generating a peptide and predicting binding affinity. We compare results for a protein of interest, PD-1, using: GANDALF, Pepcomposer, and the FDA approved drugs. We find that our method produces a peptide comparable to the FDA approved drugs and better than that of Pepcomposer. Further work will improve the GANDALF system by deepening the GAN architecture to improve on the binding affinity and 3D fit of the peptides. We are also exploring the uses of transfer learning.