摘要
在药理学和药物化学中,汉施模型是典型的定量结构关系(QSBR)问题的研究方法。汉施QSAR方程被用作电子结构和亲脂性的输出参数。在这项工作中,我们执行的汉施分析综述。我们基于微扰理论还开发了一个新的pt-qsbr Hansch的模型类型(PT)和大量报道CheMBL毒品QSBR方法。该模型预测正确的49312个53783负扰动(特异性= 91.7%)和16197个正扰动(灵敏度= 76.2%)在训练系列。该模型还预测正确的49312 / 53783(91.7%)和16197 / 21245(76.2%)负或正扰动外部验证系列。我们的模型应用在有机合成、药物分析理论的实验研究,测量的结果和预测的一系列化合物的类似Rasagiline(复合参考)具有潜在的神经保护作用。
关键词: 阿尔茨海默病、神经变性疾病、海马蛋白质组,雷沙吉兰衍生物,药物–目标结合,QSBR模型摄动理论。
图形摘要
Current Drug Targets
Title:Multi-Target Mining of Alzheimer Disease Proteome with Hansch’s QSBR-Perturbation Theory and Experimental-Theoretic Study of New Thiophene Isosters of Rasagiline
Volume: 18 Issue: 5
关键词: 阿尔茨海默病、神经变性疾病、海马蛋白质组,雷沙吉兰衍生物,药物–目标结合,QSBR模型摄动理论。
摘要: Hansch’s model is a classic approach to Quantitative Structure-Binding Relationships (QSBR) problems in Pharmacology and Medicinal Chemistry. Hansch QSAR equations are used as input parameters of electronic structure and lipophilicity. In this work, we perform a review on Hansch’s analysis. We also developed a new type of PT-QSBR Hansch’s model based on Perturbation Theory (PT) and QSBR approach for a large number of drugs reported in CheMBL. The targets are proteins expressed by the Hippocampus region of the brain of Alzheimer Disease (AD) patients. The model predicted correctly 49312 out of 53783 negative perturbations (Specificity = 91.7%) and 16197 out of 21245 positive perturbations (Sensitivity = 76.2%) in training series. The model also predicted correctly 49312/53783 (91.7%) and 16197/21245 (76.2%) negative or positive perturbations in external validation series. We applied our model in theoretical-experimental studies of organic synthesis, pharmacological assay, and prediction of unmeasured results for a series of compounds similar to Rasagiline (compound of reference) with potential neuroprotection effect.
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Multi-Target Mining of Alzheimer Disease Proteome with Hansch’s QSBR-Perturbation Theory and Experimental-Theoretic Study of New Thiophene Isosters of Rasagiline, Current Drug Targets 2017; 18 (5) . https://dx.doi.org/10.2174/1389450116666151102095243
DOI https://dx.doi.org/10.2174/1389450116666151102095243 |
Print ISSN 1389-4501 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-5592 |
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