New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase b inhibitors

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dc.contributor.author Marchand-Geneste, N.
dc.contributor.author Watson, K. A.
dc.contributor.author Alsberg, B.
dc.contributor.author King, Ross Donald
dc.date.accessioned 2008-12-17T16:38:02Z
dc.date.available 2008-12-17T16:38:02Z
dc.date.issued 2002
dc.identifier.citation Marchand-Geneste , N , Watson , K A , Alsberg , B & King , R D 2002 , ' New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase b inhibitors ' Journal of Medicinal Chemistry , vol 45 , no. 2 , pp. 399-409 . en
dc.identifier.issn 0022-2623
dc.identifier.other PURE: 96855
dc.identifier.other dspace: 2160/1757
dc.identifier.uri http://hdl.handle.net/2160/1757
dc.description New approach to pharmacophore mapping and King, Ross, Marchand-Geneste, N., Watson, K.A., Alsberg, B., (2002) 'QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase b inhibitors', Journal of Medicinal Chemistry 45(2) pp.399-409 RAE2008 en
dc.description.abstract A key problem in QSAR is the selection of appropriate descriptors to form accurate regression equations for the compounds under study. Inductive logic programming (ILP) algorithms are a class of machine-learning algorithms that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives ILP the advantages of not requiring explicit superimposition of individual compounds in a dataset, of dealing naturally with multiple conformations, and of using a language much closer to that used normally by chemists. We unify ILP and standard regression techniques to give a QSAR method that has the strength of ILP at describing steric structure with the familiarity and power of regression methods. Complex pharmacophores, correlating with activity, were identified and used as new indicator variables, along with the comparative molecular field analysis (CoMFA) prediction, to form predictive regression equations. We compared the formation of 3D-QSARs using standard CoMFA with the use of ILP on the well-studied thermolysin zinc protease inhibitor dataset and a glycogen phosphorylase inhibitor dataset. In each case the addition of ILP variables produced statistically better results (P <0.01 for thermolysin and P <0.05 for GP datasets) than the CoMFA analysis. Moreover, the new ILP variables were not found to increase the complexity of the final QSAR equations and gave possible insight into the binding mechanism of the ligand−protein complex under study. en
dc.format.extent 11 en
dc.language.iso eng
dc.relation.ispartof Journal of Medicinal Chemistry en
dc.title New approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase b inhibitors en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1021/jm0155244
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Computational Biology and Bioinformatics en
dc.description.status Peer reviewed en


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