Researcher: Kahraman, Pınar
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Kahraman, Pınar
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Publication Metadata only Classification of 1,4-dihydropyridine calcium channel antagonists using the hyperbox approach(Amer Chemical Soc, 2007) N/A; N/A; Department of Industrial Engineering; Kahraman, Pınar; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956The early prediction of activity-related characteristics of drug candidates is an important problem in drug design. Activity levels of drug candidates are classified as low or high depending on their IC50 values. Because the experimental determination of IC50 values for a vast number of molecules is both time-consuming and expensive, computational approaches are employed. In this work, we present a novel approach to classify the activities of drug molecules. We use the hyperbox classification method in combination with partial least-squares regression to determine the most relevant molecular descriptors of the drug molecules for an efficient classification. The effectiveness of the approach is illustrated on DHP derivatives. The results indicate that the proposed approach outperforms other approaches reported in the literature.Publication Metadata only Qsar analysis of 1,4-dihydropyridine calcium channel antogonists(Elsevier, 2007) Department of Industrial Engineering; N/A; Türkay, Metin; Kahraman, Pınar; Faculty Member; Master Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 24956; N/AThe early prediction of activity related characteristics of drug candidates is an important problem in drug design. The activities of drug candidates are classified as low or high depending on their IC50 values. Since experimental determination of IC50 values for a vast number of molecules is both time consuming and expensive, computational approaches are employed. In this paper, we present a novel approach to classify the activities of drug molecules. We use hyper-boxes classification method in combination with partial least squares regression to determine the most relevant molecular descriptors of the drug molecules in efficient classification. The effectiveness of the approach is illustrated on DHP derivatives. The results indicate that the proposed approach outperforms the other approaches reported in the literature.