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Molecules 2018, 23(11), 2892; https://doi.org/10.3390/molecules23112892

In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods

1
Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China
2
State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 2A Nanwei Road, Beijing 100050, China
3
National Engineering Laboratory for Advanced Municipal Wastewater Treatment & Reuse Technology, Engineering Research Center of Beijing, Beijing University of Technology, Beijing 100124, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Rino Ragno and Milan Mladenović
Received: 21 October 2018 / Revised: 4 November 2018 / Accepted: 6 November 2018 / Published: 6 November 2018
(This article belongs to the Special Issue Application of Computational Methods in Drug Design)
Full-Text   |   PDF [4737 KB, uploaded 6 November 2018]   |  

Abstract

O6-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O6-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED50 values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q2Loo = 0.83, R2 = 0.87, Q2ext = 0.67, and R2ext = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors. View Full-Text
Keywords: MGMT; anticancer alkylating agents; resistance; inhibitors; QSAR; classification MGMT; anticancer alkylating agents; resistance; inhibitors; QSAR; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Sun, G.; Fan, T.; Sun, X.; Hao, Y.; Cui, X.; Zhao, L.; Ren, T.; Zhou, Y.; Zhong, R.; Peng, Y. In Silico Prediction of O6-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods. Molecules 2018, 23, 2892.

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