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A genetic programming-based approach to identify potential inhibitors of serine protease of Mycobacterium tuberculosis

机译:基于遗传编程的方法,以鉴定结核分枝杆菌丝氨酸蛋白酶潜在抑制剂的方法

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Aim: We applied genetic programming approaches to understand the impact of descriptors on inhibitory effects of serine protease inhibitors of Mycobacterium tuberculosis (Mtb) and the discovery of new inhibitors as drug candidates. Materialsandmethods: The experimental dataset of serine protease inhibitors of Mtb descriptors was optimized by genetic algorithm (GA) along with the correlation-based feature selection (CFS) in order to develop predictive models using machine-learning algorithms. The best model was deployed on a library of 918 phytochemical compounds to screen potential serine protease inhibitors of?Mtb. Quality and performance of the predictive models were evaluated using various standard statistical parameters.?Result:?The best random forest model with CFS-GA screened 126 anti-tubercular agents out of 918 phytochemical compounds. Also, genetic programing symbolic classification method is optimized descriptors and developed an equation for mathematical models. Conclusion: The use of CFS-GA with random forest-enhanced classification accuracy and predicted new serine protease inhibitors of Mtb, which can be used for better drug development against tuberculosis.
机译:目的:我们应用遗传编程方法来了解描述符对丝杆菌蛋白酶抑制剂(MTB)的抑制作用的影响,以及作为毒品候选者的新抑制剂的发现。 MaterserAdandMethods:通过遗传算法(GA)优化了MTB描述符的丝氨酸蛋白酶抑制剂的实验数据集,以及基于相关的特征选择(CFS),以便使用机器学习算法开发预测模型。最佳模型部署在918个植物化学化合物的文库上,以筛选筛选αmTB的潜在丝氨酸蛋白酶抑制剂。使用各种标准统计参数评估预测模型的质量和性能.?ult:1,具有CFS-GA的最佳随机森林模型,筛选出96个植物化合物中的126个抗结核剂。此外,遗传编程符号分类方法是优化描述符,并为数学模型开发了一个方程。结论:使用随机森林的CFS-GA增强的分类精度和预测MTB的新型丝氨酸蛋白酶抑制剂,可用于对结核病的更好药物发育。

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