苯并噻唑
数量结构-活动关系
试验装置
腙
化学
训练集
分子描述符
可预测性
线性回归
交叉验证
生物系统
立体化学
人工智能
计算机科学
数学
机器学习
生物
生物化学
统计
作者
Pawan Gupta,Aleksandrs Gutcaits
出处
期刊:Letters in Drug Design & Discovery
[Bentham Science]
日期:2018-11-01
卷期号:16 (1): 11-20
被引量:1
标识
DOI:10.2174/1570180815666180502093039
摘要
Background: B-cell Lymphoma Extra Large (Bcl-XL) belongs to B-cell Lymphoma two (Bcl-2) family. Due to its over-expression and anti-apoptotic role in many cancers, it has been proven to be a more biologically relevant therapeutic target in anti-cancer therapy. In this study, a Quantitative Structure Activity Relationship (QSAR) modeling was performed to establish the link between structural properties and inhibitory potency of benzothiazole hydrazone derivatives against Bcl-XL. Methods: The 53 benzothiazole hydrazone derivatives have been used for model development using genetic algorithm and multiple linear regression methods. The data set is divided into training and test set using Kennard-Stone based algorithm. The best QSAR model has been selected with statistically significant r2 = 0.931, F-test =55.488 RMSE = 0.441 and Q2 0.900. Results: The model has been tested successfully for external validation (r2 pred = 0.752), as well as different criteria for acceptable model predictability. Furthermore, analysis of the applicability domain has been carried out to evaluate the prediction reliability of external set molecules. The developed QSAR model has revealed that nThiazoles, nROH, EEig13d, WA, BEHv6, HATS6m, RDF035u and IC4 descriptors are important physico-chemical properties for determining the inhibitory activity of these molecules. Conclusion: The developed QSAR model is stable for this chemical series, indicating that test set molecules represent the training dataset. The model is statistically reliable with good predictability. The obtained descriptors reflect important structural features required for activity against Bcl-XL. These properties are designated by topology, shape, size, geometry, substitution information of the molecules (nThiazoles and nROH) and electronic properties. In a nutshell, these characteristics can be successfully utilized for designing and screening of novel inhibitors.
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