F. Ansari, A. Niazi, J. Ghasemi, Atisa Yazdanipour
{"title":"乙酰胆碱酯酶抑制剂对接、2d构效关系及ADMET研究","authors":"F. Ansari, A. Niazi, J. Ghasemi, Atisa Yazdanipour","doi":"10.22036/PCR.2021.272257.1884","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":20084,"journal":{"name":"Physical Chemistry Research","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Docking and 2D-Structure-activity Relationship and ADMET Studies of Acetylcholinesterase Inhibitors\",\"authors\":\"F. Ansari, A. Niazi, J. Ghasemi, Atisa Yazdanipour\",\"doi\":\"10.22036/PCR.2021.272257.1884\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":20084,\"journal\":{\"name\":\"Physical Chemistry Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physical Chemistry Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22036/PCR.2021.272257.1884\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Chemistry Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22036/PCR.2021.272257.1884","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
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.