Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.
Department of Pathobiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61802, United States.
Curr Top Med Chem. 2019;19(1):4-16. doi: 10.2174/1568026619666190122151634.
Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.
在过去几十年中，肽作为一种治疗候选物在药物发现中受到越来越多的关注，特别是在抗菌肽 (AMPs)、抗癌肽 (ACPs) 和抗炎肽 (AIPs) 方面。人们认为，这些肽可以调节以前无法触及的各种复杂疾病。近年来，抗菌药物耐药性的关键问题促使制药行业寻找新的治疗药物。与有机小分子药物相比，基于肽的治疗方法具有高度的特异性和最小的毒性。因此，肽被广泛用于新型有效药物的设计和发现。目前，使用传统方法进行大规模的肽活性筛选既昂贵又耗时费力。因此，为了提高准确性和有效性，基于机器学习的方法已被引入来预测肽的活性。在这篇综述中，我们记录了基于机器学习的肽预测的最新进展，这将对潜在的有效 AMPs、ACPs 和 AIPs 的发现大有裨益。
