Determining the Applicability of QSPR Models to Property Prediction for Query Compounds

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Professor Peter Jurs of Pennsylvania State University is supported by a Small Grant for Exploratory Research from the Analytical and Surface Chemistry Program to use neural network learning methods to predict chemical properties. The first track consists of generating the Quantitative Structure-Property Relationship (QSPR) model by using a training set of compounds whose property values are known. The second track, which is novel to this project, consists of generating a binary classifier or similarity assessor trained to distinguish betweeen compounds whose structures are similar to those of the training set versus those whose structures are not similar to the training set. The goal is to be able to assess properties of molecules computationally so that large numbers of compounds can be screened for effectiveness in separation, sensing, biological activity, and so on. The use of computational quantitative structure/activity relationships (QSAR) is an important approach to molecular design in industry. This work seeks to improve the accuracy of prediction of important chemical properties such as toxicity and medical efficacy using chemical databases.