Recent advances and application of generative adversarial networks in drug discovery, development, and targeting

Abstract

A rising amount of research demonstrates that artificial intelligence and machine learning approaches can provide an essential basis for the drug design and discovery process. Deep learning algorithms are being developed in response to recent advances in computer technology as part of the creation of therapeutically relevant medications for the treatment of a variety of ailments. In this review, we focus on the most recent advances in the areas of drug design and discovery research employing generative deep learning methodologies such as generative adversarial network (GAN) frameworks. To begin, we examine drug design and discovery studies that use several GAN methodologies to evaluate one key application, such as molecular de novo design in drug design and discovery. Furthermore, we discuss many GAN models for dimension reduction of single-cell data at the preclinical stage of the drug development pipeline. We also show various experiments in de novo peptide and protein creation utilizing GAN frameworks. Furthermore, we discuss the limits of past drug design and discovery research employing GAN models. Finally, we give a discussion on future research prospects and obstacles.