Abstract

Exploration (factor, cluster, and decision tree), regression (multiple linear regression), and neural network (regression, classification) models of clustering, approximation and prediction of the stability constants of cation complexes with ionophore antibiotics (nonactin, monactin, dinactin, trinactin, ennatin B, monensin A, and valinomycin) according to the properties of organic solvents (methanol, ethanol, acetonitrile, and nitrobenzene) and cations (Li⁺, Na⁺, K⁺, Rb⁺, Cs⁺, Tl⁺, Ag⁺, NH₄⁺, Mg²⁺, Ca²⁺, Sr²⁺, Ba²⁺, and Mn²⁺) have been developed. It has been shown that neural network performance is better than that of multiple linear regression (the correlation coefficient on the training sample 0.756 compared to 0.697). The neural network classifier and the classification tree has confirmed the clustering of stability of ionophore–cation complexes carried out by the exploratory k-means method by 97.2%. The prognostic capabilities of the constructed multilayer perceptron have been demonstrated.

中文翻译：

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含离子载体抗生素的阳离子配合物稳定性的计算机分析

摘要

探索（因子，聚类和决策树），回归（多元线性回归）和神经网络（回归，分类）模型，对阳离子与离子载体抗生素（nonactin，monactin，dinactin，根据有机溶剂（甲醇，乙醇，乙腈和硝基苯）和阳离子（Li ⁺，Na ⁺，K ⁺，Rb ⁺，Cs ⁺，Tl ⁺，Ag ⁺，NH ₄ ⁺，Mg 2 ⁺，Ca 2 ⁺，Sr 2 ⁺，Ba ²⁺，和Mn ²⁺）已经被开发出来。结果表明，神经网络的性能优于多元线性回归（训练样本的相关系数为0.756，而相关系数为0.697）。神经网络分类器和分类树已经证实了探索性k均值方法对离子载体-阳离子配合物稳定性的聚类程度为97.2％。已经证明了构建的多层感知器的预后能力。