GANDALF: Peptide Generation for Drug Design using Sequential and Structural Generative Adversarial Networks

Abstract

Computational drug design has the potential to save time and money by providing a better starting point for new drugs with a complete computational evaluation. We propose a peptide design system for protein targets based on a Generative Adversarial Network (GAN) called GANDALF (Generative Adversarial Network Drug-TArget Ligand Fructifier). GAN based methods have been developed for computational drug design but these can only generate small molecules, not peptides. Peptides are very complex macromolecules which makes them much more difficult than small molecules to generate. Our GANDALF methodology uses two networks to generate a new peptide sequence and structure. It also incorporates data such as active atoms not used in other methods. Active atoms are important because they interact via electron sharing when a target protein and a peptide bind to each other. We can identify the active atoms using our electron structure calculation (eCADD) program and the rules of interaction we have developed. Our method goes farther than comparable methods by generating a full peptide structure as well as predicting binding affinity. The results were validated using a multi-step process comparing the results with FDA approved drugs and our initial prototype method. We have generated multiple peptides for three targets of interest (PD-1, PDL-1, and CTLA-4) and have found that the best generated peptide for each target was comparable to the FDA approved drugs in binding affinity and fitness of 3D binding as well as show the generated peptides were unique from the existing FDA drugs.