Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design.

Eugene Lin,Chieh-Hsin Lin,Hsien-Yuan Lane

doi:10.3390/molecules25143250

Abstract

A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery. In light of latest advancements in computing technologies, deep learning algorithms are being created during the development of clinically useful drugs for treatment of a number of diseases. In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks. Firstly, we review drug design and discovery studies that leverage various GAN techniques to assess one main application such as molecular de novo design in drug design and discovery. In addition, we describe various GAN models to fulfill the dimension reduction task of single-cell data in the preclinical stage of the drug development pipeline. Furthermore, we depict several studies in de novo peptide and protein design using GAN frameworks. Moreover, we outline the limitations in regard to the previous drug design and discovery studies using GAN models. Finally, we present a discussion of directions and challenges for future research.

Highlights

Nowadays researchers have been making compelling progress in the interdisciplinary fields of artificial intelligence, machine learning, and drug design and discovery [1,2,3,4]
We mainly focus on these three applications using a wide variety of the generative adversarial network (GAN)-based frameworks because, to our knowledge, there may be scant studies in drug design and discovery using the GAN-based frameworks for other applications at the time of the submission of this paper
In terms of molecular de novo design techniques, it is of great interest that future prospective research projects should concern deep learning approaches such as the GAN architecture to generate novel molecular compounds with desired molecular features, which may contribute to feasible medical solutions in public health as well as global health

Summary

Introduction

Nowadays researchers have been making compelling progress in the interdisciplinary fields of artificial intelligence, machine learning, and drug design and discovery [1,2,3,4]. In the preclinical stage of the drug development pipeline, deep learning approaches such as deep variational autoencoder [14] have been used to conduct the dimension reduction task of single-cell data for cell-specific biomarker discovery with single-cell RNA sequencing (scRNA-seq) techniques [15,16]. Another interesting example of deep learning approaches is the generation of novel chemical structures by using deep variational autoencoder during the drug screening and discovery stage [17].

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Conclusion