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首页> 外文期刊>Molecular diversity >Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)
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Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)

机译：药物设计QSAR中的遗传算法优化：贝叶斯规则遗传神经网络（BRGNN）和遗传算法优化的支持向量机（GA-SVM）

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摘要

Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.
机译：“计算机模拟”药物设计中的许多文章都采用遗传算法（GA）进行特征选择，模型优化，构象搜索或对接研究。这些文章中的一些描述了GA在结合回归和/或分类技术的定量结构-活性关系（QSAR）建模中的应用。我们回顾了GA在药物设计QSAR中的实施，特别是在优化鲁棒数学模型（例如贝叶斯正则化人工神经网络（BRANN）和支持向量机（SVM））方面针对不同药物设计问题的性能。建模数据集包括ADMET和溶解度特性，癌症靶标抑制剂，乙酰胆碱酯酶抑制剂，HIV-1蛋白酶抑制剂，离子通道和钙进入阻滞剂以及抗原生动物化合物，以及蛋白质类别，功能和构象稳定性数据。经过GA优化的预测变量通常比以前在相同数据集上发布的模型更准确，更可靠，并且可以在验证实验中解释超过65％的数据差异。另外，在大量分子描述符上的特征选择提供了对配体-靶标相互作用的结构和原子性质的了解。

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