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

Molecules

Eugene Lin¹  Chieh-Hsin Lin²  Hsien-Yuan Lane² 

[1] Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan;

关键词: artificial intelligence;    deep learning;    de novo peptide and protein design;    dimension reduction;    drug design;    generative adversarial networks;   

DOI  :  10.3390/molecules25143250

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown