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MDF - A New QSPR/QSAR Molecular Descriptors Family

By Lorentz JÄNTSCHI

Abstract

MotivationIn the present are many QSAR/QSPR models, based on varied considerations, from mathematical through topological and geometrical to 3D molecular geometry approaches.IdeaThe idea is to create a unitary approach, based on a minimal set of well-known truths, capable to generate an efficient model of property behavior depending on molecular structure.MethodFirst step in order to reach the proposed goal is to create a huge family of molecular descriptors starting from molecular structure as a graph, considering the bonds and bond types, atom types and a most probable 3D geometry of the molecule. More, using this family of molecular descriptors, a preliminary selection is done in simple linear regression with the measured property. The resulted set of valid descriptors serves for multivariate regressions in order to reach the best QSAR/QSPR model.Results The comparisons of the obtained results with other models shows that the proposed model of Molecular Descriptors Family is superior to most of the all other models.AdvantagesThe model is dependent only of the microscopic molecular structure and it can be applies at any macroscopic molecular property.For a given molecular structure or set of structures, is necessary only one calculation of the descriptors, and can be applies to more than one measured property without changes. In other words, the MDF of a molecular structure is a molecular invariant.DisadvantagesBecause the set of molecular descriptors are huge (787968 computed values), the processing time of the model finding is time consuming.ConclusionConsidering the obtained results, advantages and disadvantages and also the trend of computing performances, the MDF method promise a great expansion of using

Topics: QSAR model, Molecular descriptors, Molecular Descriptors Family, Science (General), Q1-390, Science, Q, DOAJ:Science (General), DOAJ:Science General
Publisher: AcademicDirect
Year: 2004
OAI identifier: oai:doaj.org/article:e05317672c6948029e044d92913899ee
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