Target Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks

Abstract

Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, have been developed with the purpose of picking completely new samples from a partially known space. Generative models offer high potential for designing de novo molecules; however, in order for them to be useful in real-life drug development pipelines, these models should be able to design target-specific molecules, which is the next step in this field. In this study, we propose a novel generative system, DrugGEN, for the de novo design of drug candidate molecules that interact with selected target proteins. The proposed system represents compounds and protein structures as graphs and processes them via serially connected two generative adversarial networks comprising graph transformers. DrugGEN is implemented with five independent models, each with a unique sample generation routine. The system

Mol-Zero-GAN: zero-shot adaptation of molecular generative adversarial network for specific protein targets

“…OptiMol applied Conditioning by Adaptive Sampling (CbAS) to shift the based learned distribution to maximize an objective function: the docking score of the protein target. DrugGEN Ünlü et al 19 can generate molecules with specific properties by feeding protein features into its graph transformer decoder module. The provided protein features allow DrugGEN to design target-specific molecules by incorporating the desired protein target information.…”