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.

中文翻译：

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基于机器学习的合理设计，用于高效发现allatostatin类似物作为新型IGR的有前途的主要候选药物


背景昆虫神经肽allatostatins （AST） 在调节昆虫生长、发育和繁殖中起着至关重要的作用，使其成为新的昆虫生长调节剂 （IGR） 的潜在候选者。然而，天然 AST 在害虫管理中的实际应用受到其序列长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 类似物的传统方法通常耗时且耗费资源。本研究旨在采用新的计算方法来理解结构-活性关系并有效地发现有效的 AST 类似物。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，以揭示影响 AST 类似物的保幼激素抑制活性的关键结构因素。这些模型表明，有效的 AST 类似物应分别包含苯乙烯、亲水和芳香族基团，以及位置 1、2、3 和 4 处的可旋转键。设计并合成了 6 种类似物 （A52-A57），它们表现出有效的保幼激素抑制活性 （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-AST，使其成为 IGR 的潜在主要候选者。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 先导候选物的开发，为害虫管理提供有用的参考。© 2024 化工学会.