Prediction of Mutagenicity of Aromatic and Heteroaromatic Amines from Structure: A Hierarchical QSAR Approach
Due to the lack of experimental data, there has been increasing use of theoretical structural descriptors in the hazard assessment of chemicals. We have used a hierarchical approach to develop class-specific quantitative structure−activity relationship (QSAR) models for the prediction of mutagenicit...
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| Veröffentlicht in: | Journal of Chemical Information and Computer Sciences 2001-05, Vol.41 (3), p.671-678 |
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| container_end_page | 678 |
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| container_title | Journal of Chemical Information and Computer Sciences |
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| creator | Basak, Subhash C Mills, Denise R Balaban, Alexandru T Gute, Brian D |
| description | Due to the lack of experimental data, there has been increasing use of theoretical structural descriptors in the hazard assessment of chemicals. We have used a hierarchical approach to develop class-specific quantitative structure−activity relationship (QSAR) models for the prediction of mutagenicity of a set of 95 aromatic and heteroaromatic amines. The hierarchical approach begins with the simplest molecular descriptors, the topostructural, which encode limited chemical information. The complexity is then increased, adding topochemical, geometric, and finally quantum chemical parameters. We have also added log P to the set of independent variables. The results indicate that the topological parameters, i.e., the topostructural and topochemical indices, explain the majority of the variance, and that the inclusion of log P, geometric, and quantum chemical parameters does not result in significantly improved predictive models. |
| doi_str_mv | 10.1021/ci000126f |
| format | Article |
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We have used a hierarchical approach to develop class-specific quantitative structure−activity relationship (QSAR) models for the prediction of mutagenicity of a set of 95 aromatic and heteroaromatic amines. The hierarchical approach begins with the simplest molecular descriptors, the topostructural, which encode limited chemical information. The complexity is then increased, adding topochemical, geometric, and finally quantum chemical parameters. We have also added log P to the set of independent variables. The results indicate that the topological parameters, i.e., the topostructural and topochemical indices, explain the majority of the variance, and that the inclusion of log P, geometric, and quantum chemical parameters does not result in significantly improved predictive models.</description><identifier>ISSN: 0095-2338</identifier><identifier>EISSN: 1549-960X</identifier><identifier>DOI: 10.1021/ci000126f</identifier><identifier>PMID: 11410045</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Algorithms ; Databases, Factual ; Heterocyclic Compounds - chemistry ; Heterocyclic Compounds - toxicity ; Hydrocarbons, Aromatic - chemistry ; Hydrocarbons, Aromatic - toxicity ; Mutagens - chemistry ; Mutagens - toxicity ; Quantitative Structure-Activity Relationship ; Quantum Theory ; Salmonella typhimurium - drug effects ; Salmonella typhimurium - genetics</subject><ispartof>Journal of Chemical Information and Computer Sciences, 2001-05, Vol.41 (3), p.671-678</ispartof><rights>Copyright © 2001 American Chemical Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a349t-45cf10eecf09cc70ffa771636c3c6a8af39d4f728fedc657f93484645cf38fa23</citedby><cites>FETCH-LOGICAL-a349t-45cf10eecf09cc70ffa771636c3c6a8af39d4f728fedc657f93484645cf38fa23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/ci000126f$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/ci000126f$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,776,780,2752,27055,27903,27904,56715,56765</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/11410045$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Basak, Subhash C</creatorcontrib><creatorcontrib>Mills, Denise R</creatorcontrib><creatorcontrib>Balaban, Alexandru T</creatorcontrib><creatorcontrib>Gute, Brian D</creatorcontrib><title>Prediction of Mutagenicity of Aromatic and Heteroaromatic Amines from Structure: A Hierarchical QSAR Approach</title><title>Journal of Chemical Information and Computer Sciences</title><addtitle>J. Chem. Inf. Comput. Sci</addtitle><description>Due to the lack of experimental data, there has been increasing use of theoretical structural descriptors in the hazard assessment of chemicals. We have used a hierarchical approach to develop class-specific quantitative structure−activity relationship (QSAR) models for the prediction of mutagenicity of a set of 95 aromatic and heteroaromatic amines. The hierarchical approach begins with the simplest molecular descriptors, the topostructural, which encode limited chemical information. The complexity is then increased, adding topochemical, geometric, and finally quantum chemical parameters. We have also added log P to the set of independent variables. The results indicate that the topological parameters, i.e., the topostructural and topochemical indices, explain the majority of the variance, and that the inclusion of log P, geometric, and quantum chemical parameters does not result in significantly improved predictive models.</description><subject>Algorithms</subject><subject>Databases, Factual</subject><subject>Heterocyclic Compounds - chemistry</subject><subject>Heterocyclic Compounds - toxicity</subject><subject>Hydrocarbons, Aromatic - chemistry</subject><subject>Hydrocarbons, Aromatic - toxicity</subject><subject>Mutagens - chemistry</subject><subject>Mutagens - toxicity</subject><subject>Quantitative Structure-Activity Relationship</subject><subject>Quantum Theory</subject><subject>Salmonella typhimurium - drug effects</subject><subject>Salmonella typhimurium - genetics</subject><issn>0095-2338</issn><issn>1549-960X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNptkLtOxDAQRS0EYpdHwQ8gNxQUATtOnJguIGDRsuIRkOgsM7FZA3nIcSToaPlNvoSsAktDNboz585oLkI7lBxQEtJDsIQQGnKzgsY0jkQgOHlYRWNCRByEjKUjtNG2z4QwJni4jkaURpSQKB6j5trpwoK3dYVrg2edV0-6smD9-0Jnri6Vt4BVVeCJ9trV6reVlbbSLTa9xrl3HfjO6aOvj0-c4YnVTjmYW1Cv-CbPbnHWNL0X5ltozajXVm__1E10f3Z6dzIJLq_OL06yy0CxSPggisFQojUYIgASYoxKEsoZBwZcpcowUUQmCVOjC-BxYgSL0ogvbCw1KmSbaH_YC65uW6eNbJwtlXuXlMhFanKZWs_uDmzTPZa6-CN_YuqBYABs6_Xbcq7ci-QJS2J5d53LqUhn03B2LPOe3xt4Ba18rjtX9a_-c_gbHwyEtg</recordid><startdate>20010501</startdate><enddate>20010501</enddate><creator>Basak, Subhash C</creator><creator>Mills, Denise R</creator><creator>Balaban, Alexandru T</creator><creator>Gute, Brian D</creator><general>American Chemical Society</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20010501</creationdate><title>Prediction of Mutagenicity of Aromatic and Heteroaromatic Amines from Structure: A Hierarchical QSAR Approach</title><author>Basak, Subhash C ; Mills, Denise R ; Balaban, Alexandru T ; Gute, Brian D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a349t-45cf10eecf09cc70ffa771636c3c6a8af39d4f728fedc657f93484645cf38fa23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Algorithms</topic><topic>Databases, Factual</topic><topic>Heterocyclic Compounds - chemistry</topic><topic>Heterocyclic Compounds - toxicity</topic><topic>Hydrocarbons, Aromatic - chemistry</topic><topic>Hydrocarbons, Aromatic - toxicity</topic><topic>Mutagens - chemistry</topic><topic>Mutagens - toxicity</topic><topic>Quantitative Structure-Activity Relationship</topic><topic>Quantum Theory</topic><topic>Salmonella typhimurium - drug effects</topic><topic>Salmonella typhimurium - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Basak, Subhash C</creatorcontrib><creatorcontrib>Mills, Denise R</creatorcontrib><creatorcontrib>Balaban, Alexandru T</creatorcontrib><creatorcontrib>Gute, Brian D</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><jtitle>Journal of Chemical Information and Computer Sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Basak, Subhash C</au><au>Mills, Denise R</au><au>Balaban, Alexandru T</au><au>Gute, Brian D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of Mutagenicity of Aromatic and Heteroaromatic Amines from Structure: A Hierarchical QSAR Approach</atitle><jtitle>Journal of Chemical Information and Computer Sciences</jtitle><addtitle>J. Chem. Inf. Comput. Sci</addtitle><date>2001-05-01</date><risdate>2001</risdate><volume>41</volume><issue>3</issue><spage>671</spage><epage>678</epage><pages>671-678</pages><issn>0095-2338</issn><eissn>1549-960X</eissn><abstract>Due to the lack of experimental data, there has been increasing use of theoretical structural descriptors in the hazard assessment of chemicals. We have used a hierarchical approach to develop class-specific quantitative structure−activity relationship (QSAR) models for the prediction of mutagenicity of a set of 95 aromatic and heteroaromatic amines. The hierarchical approach begins with the simplest molecular descriptors, the topostructural, which encode limited chemical information. The complexity is then increased, adding topochemical, geometric, and finally quantum chemical parameters. We have also added log P to the set of independent variables. The results indicate that the topological parameters, i.e., the topostructural and topochemical indices, explain the majority of the variance, and that the inclusion of log P, geometric, and quantum chemical parameters does not result in significantly improved predictive models.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>11410045</pmid><doi>10.1021/ci000126f</doi><tpages>8</tpages></addata></record> |
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| ispartof | Journal of Chemical Information and Computer Sciences, 2001-05, Vol.41 (3), p.671-678 |
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| language | eng |
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| source | ACS Publications; MEDLINE |
| subjects | Algorithms Databases, Factual Heterocyclic Compounds - chemistry Heterocyclic Compounds - toxicity Hydrocarbons, Aromatic - chemistry Hydrocarbons, Aromatic - toxicity Mutagens - chemistry Mutagens - toxicity Quantitative Structure-Activity Relationship Quantum Theory Salmonella typhimurium - drug effects Salmonella typhimurium - genetics |
| title | Prediction of Mutagenicity of Aromatic and Heteroaromatic Amines from Structure: A Hierarchical QSAR Approach |
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