Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada.
Department of Microbiology and Infectious Diseases, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada.
Genes (Basel). 2023 May 29;14(6):1194. doi: 10.3390/genes14061194.
Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics.
利用计算在肽治疗药物的开发中得到了越来越多的认可，是一种为与疾病相关的靶点生成新型治疗药物的有价值的工具。为此，计算通过鉴定具有增强的药代动力学特性和降低毒性的新型治疗药物，改变了肽设计领域。肽设计的过程涉及分子对接、分子动力学模拟和机器学习算法的应用。三种主要的肽治疗设计方法，包括基于结构的、蛋白质模拟和短基序设计，已被广泛采用。尽管该领域取得了持续的进展，但在肽设计方面仍存在着重大挑战，包括：提高计算方法的准确性；提高临床前和临床试验的成功率；以及开发更好的策略来预测药代动力学和毒性。在这篇综述中，我们讨论了过去和现在与肽治疗药物的设计和开发相关的研究，以及计算和人工智能在未来疾病治疗中的潜力。
