3D QSAR studies on peroxisome proliferator-activated receptor gamma agonists using CoMFA and CoMSIA
The peroxisome proliferator-activated receptors (PPARs) have increasingly become attractive targets for developing novel anti-type 2 diabetic drugs. We employed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to study three-dimensional quan...
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Veröffentlicht in: | Journal of molecular modeling 2004-06, Vol.10 (3), p.165-177 |
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creator | Liao, Chenzhong Xie, Aihua Zhou, Jiaju Shi, Leming Li, Zhibin Lu, Xian-Ping |
description | The peroxisome proliferator-activated receptors (PPARs) have increasingly become attractive targets for developing novel anti-type 2 diabetic drugs. We employed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to study three-dimensional quantitative structure-activity relationship (3D QSAR) based on existing agonists of PPARgamma (including five thiazolidinediones and 74 tyrosine-based compounds). Predictive 3D QSAR models with conventional r2 and cross-validated coefficient (q2) values up to 0.974 and 0.642 for CoMFA and 0.979 and 0.686 for COMSIA were established using the SYBYL package. These models were validated by a test set containing 18 compounds. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding pockets of PPARgamma, which demonstrates that the 3D QSAR models built here are very useful in predicting activities of novel compounds for activating PPARgamma. |
doi_str_mv | 10.1007/s00894-003-0175-4 |
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We employed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to study three-dimensional quantitative structure-activity relationship (3D QSAR) based on existing agonists of PPARgamma (including five thiazolidinediones and 74 tyrosine-based compounds). Predictive 3D QSAR models with conventional r2 and cross-validated coefficient (q2) values up to 0.974 and 0.642 for CoMFA and 0.979 and 0.686 for COMSIA were established using the SYBYL package. These models were validated by a test set containing 18 compounds. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding pockets of PPARgamma, which demonstrates that the 3D QSAR models built here are very useful in predicting activities of novel compounds for activating PPARgamma.</description><identifier>EISSN: 0948-5023</identifier><identifier>DOI: 10.1007/s00894-003-0175-4</identifier><identifier>PMID: 15022104</identifier><language>eng</language><publisher>Germany</publisher><subject>Computer Simulation ; Humans ; Hydrogen Bonding ; Imaging, Three-Dimensional ; Kinetics ; Ligands ; Models, Chemical ; Models, Molecular ; Molecular Structure ; PPAR gamma - agonists ; Quantitative Structure-Activity Relationship ; Software ; Stereoisomerism ; Structural Homology, Protein</subject><ispartof>Journal of molecular modeling, 2004-06, Vol.10 (3), p.165-177</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15022104$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liao, Chenzhong</creatorcontrib><creatorcontrib>Xie, Aihua</creatorcontrib><creatorcontrib>Zhou, Jiaju</creatorcontrib><creatorcontrib>Shi, Leming</creatorcontrib><creatorcontrib>Li, Zhibin</creatorcontrib><creatorcontrib>Lu, Xian-Ping</creatorcontrib><title>3D QSAR studies on peroxisome proliferator-activated receptor gamma agonists using CoMFA and CoMSIA</title><title>Journal of molecular modeling</title><addtitle>J Mol Model</addtitle><description>The peroxisome proliferator-activated receptors (PPARs) have increasingly become attractive targets for developing novel anti-type 2 diabetic drugs. We employed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to study three-dimensional quantitative structure-activity relationship (3D QSAR) based on existing agonists of PPARgamma (including five thiazolidinediones and 74 tyrosine-based compounds). Predictive 3D QSAR models with conventional r2 and cross-validated coefficient (q2) values up to 0.974 and 0.642 for CoMFA and 0.979 and 0.686 for COMSIA were established using the SYBYL package. These models were validated by a test set containing 18 compounds. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding pockets of PPARgamma, which demonstrates that the 3D QSAR models built here are very useful in predicting activities of novel compounds for activating PPARgamma.</description><subject>Computer Simulation</subject><subject>Humans</subject><subject>Hydrogen Bonding</subject><subject>Imaging, Three-Dimensional</subject><subject>Kinetics</subject><subject>Ligands</subject><subject>Models, Chemical</subject><subject>Models, Molecular</subject><subject>Molecular Structure</subject><subject>PPAR gamma - agonists</subject><subject>Quantitative Structure-Activity Relationship</subject><subject>Software</subject><subject>Stereoisomerism</subject><subject>Structural Homology, Protein</subject><issn>0948-5023</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNo1kE1LxDAYhIMg7rLuD_AiOXmLvkn6eSyrqwsroqvnkjZvSqRtatKK_nsrrqcZhodhGEIuOFxzgPQmAGR5xAAkA57GLDohS8ijjMUg5IKsQ3gHAC7iJBbijCz4nAsO0ZLU8pY-H4oXGsZJWwzU9XRA775scB3SwbvWGvRqdJ6perSfakRNPdY4zBFtVNcpqhrX2zAGOgXbN3TjHrcFVb3-dYddcU5OjWoDro-6Im_bu9fNA9s_3e82xZ4NAtKRiURIkSSR1HWCqLMMpayUVghVLBBNnHItkywVsuIq4iA5GjAGY4PG5FLKFbn6651Xf0wYxrKzoca2VT26KZQpz1Mu8mgGL4_gVHWoy8HbTvnv8v8W-QNtimOq</recordid><startdate>200406</startdate><enddate>200406</enddate><creator>Liao, Chenzhong</creator><creator>Xie, Aihua</creator><creator>Zhou, Jiaju</creator><creator>Shi, Leming</creator><creator>Li, Zhibin</creator><creator>Lu, Xian-Ping</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope></search><sort><creationdate>200406</creationdate><title>3D QSAR studies on peroxisome proliferator-activated receptor gamma agonists using CoMFA and CoMSIA</title><author>Liao, Chenzhong ; Xie, Aihua ; Zhou, Jiaju ; Shi, Leming ; Li, Zhibin ; Lu, Xian-Ping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p207t-262326643dc6eed88e33badae0b52eef571d368723b1a41031ef0ffe5feff9333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Computer Simulation</topic><topic>Humans</topic><topic>Hydrogen Bonding</topic><topic>Imaging, Three-Dimensional</topic><topic>Kinetics</topic><topic>Ligands</topic><topic>Models, Chemical</topic><topic>Models, Molecular</topic><topic>Molecular Structure</topic><topic>PPAR gamma - agonists</topic><topic>Quantitative Structure-Activity Relationship</topic><topic>Software</topic><topic>Stereoisomerism</topic><topic>Structural Homology, Protein</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liao, Chenzhong</creatorcontrib><creatorcontrib>Xie, Aihua</creatorcontrib><creatorcontrib>Zhou, Jiaju</creatorcontrib><creatorcontrib>Shi, Leming</creatorcontrib><creatorcontrib>Li, Zhibin</creatorcontrib><creatorcontrib>Lu, Xian-Ping</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of molecular modeling</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liao, Chenzhong</au><au>Xie, Aihua</au><au>Zhou, Jiaju</au><au>Shi, Leming</au><au>Li, Zhibin</au><au>Lu, Xian-Ping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>3D QSAR studies on peroxisome proliferator-activated receptor gamma agonists using CoMFA and CoMSIA</atitle><jtitle>Journal of molecular modeling</jtitle><addtitle>J Mol Model</addtitle><date>2004-06</date><risdate>2004</risdate><volume>10</volume><issue>3</issue><spage>165</spage><epage>177</epage><pages>165-177</pages><eissn>0948-5023</eissn><abstract>The peroxisome proliferator-activated receptors (PPARs) have increasingly become attractive targets for developing novel anti-type 2 diabetic drugs. We employed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to study three-dimensional quantitative structure-activity relationship (3D QSAR) based on existing agonists of PPARgamma (including five thiazolidinediones and 74 tyrosine-based compounds). Predictive 3D QSAR models with conventional r2 and cross-validated coefficient (q2) values up to 0.974 and 0.642 for CoMFA and 0.979 and 0.686 for COMSIA were established using the SYBYL package. These models were validated by a test set containing 18 compounds. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding pockets of PPARgamma, which demonstrates that the 3D QSAR models built here are very useful in predicting activities of novel compounds for activating PPARgamma.</abstract><cop>Germany</cop><pmid>15022104</pmid><doi>10.1007/s00894-003-0175-4</doi><tpages>13</tpages></addata></record> |
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subjects | Computer Simulation Humans Hydrogen Bonding Imaging, Three-Dimensional Kinetics Ligands Models, Chemical Models, Molecular Molecular Structure PPAR gamma - agonists Quantitative Structure-Activity Relationship Software Stereoisomerism Structural Homology, Protein |
title | 3D QSAR studies on peroxisome proliferator-activated receptor gamma agonists using CoMFA and CoMSIA |
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