Quantitative structure activity relationship and docking studies of imidazole-based derivatives as P-glycoprotein inhibitors

Ghandadi, Morteza; Shayanfar, Ali; Hamzeh‐Mivehroud, Maryam; Jouyban, Abolghasem

doi:10.1007/s00044-014-1029-6

Cited by 11 publications

(7 citation statements)

References 52 publications

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“…Hence, classification of Pgp-interacting compounds is challenging (Wang et al, 2005[ 49 ]) and is a growing research area. Recently, many computational approaches such as quantitative structure activity relationship (Ghandadi et al, 2014[ 13 ]; Palestro et al, 2014[ 30 ]; Shen et al, 2014[ 38 ]), classification models (Adenot and Lahana, 2004[ 2 ]; Chen et al, 2011[ 8 ]; Klepsch et al, 2014[ 17 ]; Levatić et al, 2013[ 23 ]; Li et al, 2014[ 24 ]; Penzotti et al, 2002[ 31 ]; Prachayasittikul et al, 2015[ 34 ]; Wang et al, 2011[ 51 ]), molecular docking (Ghandadi et al, 2014[ 13 ]; Palestro et al, 2014[ 30 ]; Zeino et al, 2014[ 53 ]), and substructure analysis (Prachayasittikul et al, 2016[ 33 ]; Wang et al, 2011[ 51 ]; Klepsch et al, 2014[ 17 ]) have been successfully employed to provide deeper understanding about this promiscuous protein.…”

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confidence: 99%

P-glycoprotein transporter in drug development

Prachayasittikul

2016

EXCLI Journal; 15:Doc113; ISSN 1611-2156

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confidence: 99%

P-glycoprotein transporter in drug development

Prachayasittikul

2016

EXCLI Journal; 15:Doc113; ISSN 1611-2156

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“…The q 2 (LOO) and r 2 -q 2 (LOO) are other measurement criteria for evaluating the performance of QSAR models, which should be higher than 0.5 and 0.3, respectively. [58][59][60] The calculated values of these parameters for equations 6 and 7 are 0.483, 0.206 and 0.530, 0.210, respectively. The generated QSAR models are the result of the GA-MLR methodology based on a uni-objective (i.e., F) optimization function.…”

Section: Resultsmentioning

confidence: 99%

A QSAR Study on the 4-Substituted Coumarins as Potent Tubulin Polymerization Inhibitors

Dinparast

Dastmalchi

2020

Adv Pharm Bull

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Purpose: Despite the discovery and synthesis of several anticancer drugs, cancer is still a major life threatening incident for human beings after cardiovascular diseases. Toxicity, severe side effects, and drug resistance are serious problems of available commercial anticancer drugs. Coumarins are synthetic and natural heterocycles that show promising antiproliferative activities against various tumors. The aim of this research is to computationally study the coumarin derivatives in order to develop reliable quantitative structure-activity relationship (QSAR) models for predicting their anticancer activities. Methods: A data set of thirty one coumarin analogs with significant antiproliferative activities toward HepG2 cells were selected from the literature. The molecular descriptors for these compounds were calculated using Dragon, HyperChem, and ACD/Labs programs. Genetic algorithm (GA) accompanied by multiple linear regression (MLR) for simultaneous feature selection and model development was employed for generating the QSAR models. Results: Based on the obtained results, the developed linear QSAR models with three and four descriptors showed good predictive power with r2 values of 0.670 and 0.692, respectively. Moreover, the calculated validation parameters for the models confirmed the reliability of the QSAR models. Conclusion: The findings of the current study could be useful for the design and synthesis of novel anticancer drugs based on coumarin structure.

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“…Over the past few decades, more robust models have been established to predict various ADME properties, including membrane permeability, 1,2 intestinal absorption (IA), 3,4 oral bioavailability (OB), 5–7 human ether-a-go-go related gene binders, 8–10 as well as transporter binders. 11,12 Most of these models were based on quantitative-structure active relationship (QSAR) approach, ranging from simple multiple linear regression to complex machine learning techniques, such as partial least squares discriminant analysis (PLSDA), 13 naive bayes (NB) classifier, 10,14 Kohonen self-organizing maps, 15 k nearest neighbor (KNN), 5,16,17 artificial neural networks (NNET), 18 support vector machine (SVM), 5,16,17,19 and random forest (RF). 5,16,17 However, there remain some challenges when developing these methods.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive ensemble classification framework for ADME prediction

Yang

Shi

et al. 2018

RSC Adv.

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It has now become clear that in silico prediction of ADME (absorption, distribution, metabolism, and elimination) characteristics is an important component of the drug discovery process. Therefore, there has been considerable interest in the development of in silico modeling of ADME prediction in recent years. Despite the advances in this field, there remains challenges when facing the unbalanced and high dimensionality problems simultaneously. In this work, we introduce a novel adaptive ensemble classification framework named as AECF to deal with the above issues. AECF includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble. We considered five sampling methods, seven base modeling techniques, and ten ensemble rules to build a choice pool. The proper route of constructing predictive models was determined automatically according to the imbalance ratio (IR). With the adaptive characteristics of AECF, it can be used to work on the different kinds of ADME data, and the balanced data is a special case in AECF. We evaluated the performance of our approach using five extensive ADME datasets concerning Caco-2 cell permeability (CacoP), human intestinal absorption (HIA), oral bioavailability (OB), and Pglycoprotein (P-gp) binders (substrates/inhibitors, PS/PI). The performance of AECF was evaluated on two independent datasets, and the average AUC values were 0.8574-0.8602, 0.8968-0.9182, 0.7821-0.7981, 0.8139-0.8311, and 0.8874-0.8898 for CacoP, HIA, OB, PS and PI, respectively. Our results show that AECF can provide better performance and generality compared with individual models and two representative ensemble methods bagging and boosting. Furthermore, the degree of complementarity among the AECF ensemble members was investigated for the purpose of elucidating the potential advantages of our framework. We found that AECF can effectively select complementary members to construct predictive models by our auto-adaptive optimization approach, and the additional diversity in both sample and feature space mainly contribute to the complementarity of ensemble members.

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Quantitative structure activity relationship and docking studies of imidazole-based derivatives as P-glycoprotein inhibitors

Cited by 11 publications

References 52 publications

P-glycoprotein transporter in drug development

P-glycoprotein transporter in drug development

A QSAR Study on the 4-Substituted Coumarins as Potent Tubulin Polymerization Inhibitors

A novel adaptive ensemble classification framework for ADME prediction

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