基于机器学习的合理设计,高效发现有希望成为新型 IGR 候选先导药物的别他司汀类似物

IF 3.8 1区 农林科学 Q1 AGRONOMY
Yi‐Meng Zhang, Qi He, Jia‐Lin Cui, Yan Liu, Mei‐Zi Wang, Xing‐Xing Lu, Shi‐Xiang Pan, Chandni Iqbal, De‐Xing Ye, Wen‐Yu Sun, Xin‐Yuan Zhang, Zhen‐Peng Kai, Li Zhang, Xin‐Ling Yang
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引用次数: 0

摘要

背景昆虫神经肽类异雄激素(ASTs)在调节昆虫的生长、发育和繁殖方面发挥着重要作用,因此有可能成为新的昆虫生长调节剂(IGRs)。然而,天然 AST 在害虫管理中的实际应用受到其长序列和高生产成本的限制,因此开发序列更短、成本更低的 AST 类似物至关重要。设计 AST 类似物的传统方法往往耗费大量时间和资源。结果利用多元线性回归和支持向量机构建了两个机器学习模型,揭示了影响 AST 类似物幼年激素抑制活性的关键结构因素。这些模型表明,强效的 AST 类似物应在 1、2、3 和 4 号位置分别含有苯乙烯基团、亲水基团、芳香基团和可旋转键。研究人员设计并合成了六种类似物(A52-A57),这些类似物具有很强的幼年激素抑制活性(IC50 < 16 nM)。值得注意的是,类似物 A53 显示出最佳活性(IC50 = 2.07 nM),超过了大多数天然 Dippu-ASTs 的活性,使其成为 IGRs 的潜在候选先导物。该研究阐明了基于机器学习的策略如何促进 AST 类似物作为新型 IGR 候选先导药的开发,为害虫管理提供了有益的参考。© 2024 化学工业协会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning‐based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs

Machine learning‐based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs
BACKGROUNDInsect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potential candidates for new insect growth regulators (IGRs). However, the practical use of natural ASTs in pest management is constrained by their long sequences and high production costs, thus the development of AST analogs with shorter sequences and reduced cost is essential. Traditional methods for designing AST analogs are often time‐consuming and resource‐intensive. This study aims to employ new computational methodologies to understand the structure–activity relationship and efficiently discover potent AST analogs.RESULTSTwo machine learning models, utilizing multiple linear regression and support vector machine, were constructed to reveal the key structural factors that influence the juvenile hormone‐inhibiting activity of AST analogs. These models suggested that a potent AST analog should contain styrene, hydrophilic, and aromatic groups, and rotatable bonds at positions 1, 2, 3, and 4, respectively. Six analogs (A52‐A57) were designed and synthesized, and they exhibited potent juvenile hormone‐inhibiting activity (IC50 < 16 nM). Notably, analog A53 showed the best activity (IC50 = 2.07 nM), surpassing that of most natural Dippu‐ASTs, making it a potential lead candidate for IGRs.CONCLUSIONThese models promote the efficient design, screening, and prioritization of new or untested AST analogs. The study clarifies how a machine learning‐based strategy facilitates the development of AST analogs as novel IGR lead candidates, offering a useful reference for pest management. © 2024 Society of Chemical Industry.
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来源期刊
Pest Management Science
Pest Management Science 农林科学-昆虫学
CiteScore
7.90
自引率
9.80%
发文量
553
审稿时长
4.8 months
期刊介绍: Pest Management Science is the international journal of research and development in crop protection and pest control. Since its launch in 1970, the journal has become the premier forum for papers on the discovery, application, and impact on the environment of products and strategies designed for pest management. Published for SCI by John Wiley & Sons Ltd.
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