De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update

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题名:	De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update
作者:	Lin, E;Lin, E;CH, Lin;CH, Lin;藍先元;Lane, Hsien Yuan
贡献者:	醫學暨健康學院心理學系
关键词:	artificial intelligence； computer-aided drug design and discovery； deep artificial neural networks deep learning； de novo peptide design； de novo protein design； generative adversarial networks； generative chemistry ；generative methods； machine learning
日期:	2022-02-01
上传时间:	2023-03-28 01:08:25 (UTC+0)
出版者:	亞洲大學
摘要:	Nowadays, machine learning and deep learning approaches are widely utilized for generative chemistry and computer-aided drug design and discovery such as de novo peptide and protein design, where target-specific peptide-based/protein-based therapeutics have been suggested to cause fewer adverse effects than the traditional small-molecule drugs. In light of current advancements in deep learning techniques, generative adversarial network (GAN) algorithms are being leveraged to a wide variety of applications in the process of generative chemistry and computer-aided drug design and discovery. In this review, we focus on the up-to-date developments for de novo peptide and protein design research using GAN algorithms in the interdisciplinary fields of generative chemistry, machine learning, deep learning, and computer-aided drug design and discovery. First, we present various studies that investigate GAN algorithms to fulfill the task of de novo peptide and protein design in the drug development pipeline. In addition, we summarize the drawbacks with respect to the previous studies in de novo peptide and protein design using GAN algorithms. Finally, we depict a discussion of open challenges and emerging problems for future research.
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