乙酰胆碱酯酶抑制剂对接、2d构效关系及ADMET研究

IF 1.4 Q3 CHEMISTRY, MULTIDISCIPLINARY
F. Ansari, A. Niazi, J. Ghasemi, Atisa Yazdanipour
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引用次数: 1

摘要

本文利用配体-受体相互作用空间,建立了一些乙酰胆碱酯酶抑制剂的定量构效关系模型。描述符由每个分子的多变量图像分析(MIA)获得。进行对接研究以确定抑制剂的最佳构象。在第一步中,选择所有配体的最佳位姿。随后,利用配体-受体互连数据开发了MIA-QSAR模型。通过主成分分析(PCA)对描述符池进行压缩。采用遗传算法进行变量选择,然后采用支持向量机(SVM)回归方法建立模型。通过包含11个单独化合物的验证集对模型的预测能力进行了验证。PCA-GA-SVM模型的Q2、r2和∆r_m^2检验预测值分别为0.62、0.89和0.145。在用所有统计数据验证结果后,利用MIA-QSAR模型设计了三种新分子。之后,新的分子停靠在乙酰胆碱酯酶活性位点上。对接研究表明,TYR70、TYR121、TYR334、TRP279、PHE288、PHE290、TRP84、TRP334和SER286是该配合物中的活性氨基酸。最后,计算了新化合物的ADMET参数,均在可接受范围内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Docking and 2D-Structure-activity Relationship and ADMET Studies of Acetylcholinesterase Inhibitors
In this work, a quantitative structure-activity relationship (QSAR) for some tacrine derivatives inhibitors of acetylcholinesterase was modeled using ligand-receptor interconnection interaction space. The descriptors were obtained by multivariate image analysis (MIA) of each molecule. Docking studies were performed to determine the best conformers of inhibitors. In the first step, the best pose of all the ligands was selected. Afterward, an MIA-QSAR model using ligand-receptor interconnection data was developed. The pool of descriptors was compressed by principal component analysis (PCA). Variable selection was carried out by genetic algorithm (GA) followed by model building using the support vector machine (SVM) regression method. The validation of the model's predictive ability was studied by a validation set containing 11 individual compounds. The Q2, r2 and, ∆r_m^2 test prediction values for PCA-GA-SVM model were 0.62, 0.89 and 0.145, respectively. After validating the results with all statistical data, three new molecules were designed by the MIA-QSAR model. Afterward, new molecules docked in the AChE active site. Docking studies were showed the amino acids TYR70, TYR121, TYR334, TRP279, PHE288, PHE290, TRP84, TRP334, and SER286 are active amino acids in the complex. Finally, the ADMET parameters of the new compounds were calculated and were in acceptable ranges.
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来源期刊
Physical Chemistry Research
Physical Chemistry Research CHEMISTRY, MULTIDISCIPLINARY-
CiteScore
2.70
自引率
8.30%
发文量
18
期刊介绍: The motivation for this new journal is the tremendous increasing of useful articles in the field of Physical Chemistry and the related subjects in recent years, and the need of communication between Physical Chemists, Physicists and Biophysicists. We attempt to establish this fruitful communication and quick publication. High quality original papers in English dealing with experimental, theoretical and applied research related to physics and chemistry are welcomed. This journal accepts your report for publication as a regular article, review, and Letter. Review articles discussing specific areas of physical chemistry of current chemical or physical importance are also published. Subjects of Interest: Thermodynamics, Statistical Mechanics, Statistical Thermodynamics, Molecular Spectroscopy, Quantum Chemistry, Computational Chemistry, Physical Chemistry of Life Sciences, Surface Chemistry, Catalysis, Physical Chemistry of Electrochemistry, Kinetics, Nanochemistry and Nanophysics, Liquid Crystals, Ionic Liquid, Photochemistry, Experimental article of Physical chemistry. Mathematical Chemistry.
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