A novel workflow for the inverse QSPR problem using multiobjective optimization
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A workflow for the inverse quantitative structure–property relationship (QSPR) problem is reported in this paper for the de novo design of novel chemical entities (NCE) in silico through the application of existing QSPR models to calculate multiple objectives, including prediction confidence measures, to be optimized during the de novo design process. Two physical property datasets are applied as case studies of the inverse QSPR workflow (IQW): mean molecular polarizability and aqueous solubility. The case studies demonstrate the optimization of molecular structures to within a property range of interest; the optimized structures are then validated against QSPR models that are generated from sets of alternative descriptors to those used in the IQW. The paper concludes with a discussion of the results from the case studies.
KeywordsInverse QSPR problem QSAR Genetic algorithms De novo design Partial least squares regression
This research has been supported by a Marie Curie Fellowship of the European Community programme ‘Exploring leads in combinatorial catalysis for novel clean pharmaceutical/fine chemical processes’ under contract number HPMI-CT-2001-00108.
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