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Title: Methods to Improve Virtual Screening of Potential Drug Leads for Specific Pharmacodynamic and Toxicological Properties
Keywords: QSAR, ensemble methods, prediction, toxicity, drug development, support vector machine
Issue Date: 12-Aug-2011
Citation: LIEW CHIN YEE (2011-08-12). Methods to Improve Virtual Screening of Potential Drug Leads for Specific Pharmacodynamic and Toxicological Properties. ScholarBank@NUS Repository.
Abstract: As drug development is time consuming and costly, compounds that are likely to fail should be weeded out early through the use of assays and toxicity screens. Computational method is a favourable complementary technique. Nevertheless, it is not exploited to its full potential due to: models that were built from small data sets, a lack of applicability domain (AD), not being readily available for use, or not following the OECD QSAR validation guidelines. This thesis attempts to address these problems with the following strategies. First, the data augmentation approach using putative negatives was used to increase the information content of training examples without generating new experimental data. Second, ensemble methods were investigated as the approach to improve accuracies of QSAR models. Third, predictive models are to be built from data sets as large as possible, with the application of AD to define the usability of these models. Next, the QSAR models were built according to the guidance set out by the OECD. Last, the models were packaged into a free software to facilitate independent evaluation and comparison of QSAR models. The usefulness of these strategies was evaluated using pharmacodynamic data sets such as lymphocyte-specific protein tyrosine kinase inhibitors (Lck) and phosphoinositide 3-kinase inhibitors (PI3K). Further investigated were toxicological data sets such as eye and skin irritation, compounds that produce reactive metabolites, and hepatotoxicity. To the best of our knowledge, the Lck and PI3K studies were the first to produce virtual screening models from significantly larger training data with the effects of increased AD and reduced false positive hits. In addition, all models produced for toxicity prediction were better than most models of previous studies in terms of either prediction accuracy, presence of AD, data diversity, or adherence to OECD principles for the validation of QSAR. The various approaches examined are useful, to varying extents, for improving the virtual screening of potential drug leads for specific pharmacodynamic and toxicological properties.
Appears in Collections:Ph.D Theses (Open)

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