Researcher:
Özdemir, Muhittin Emre

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Master Student

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Muhittin Emre

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Özdemir

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Özdemir, Muhittin Emre

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2008 - 20092

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Researcher Of Publications: search.filters.isAuthorOfPublication.7d7b1fb0-0a3e-4487-aa02-ab7a48f4b218

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    Discovery of novel CYP17 inhibitors for the treatment of prostate cancer with structure-based drug design
    (Bentham Science Publ Ltd, 2009) N/A; N/A; N/A; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Armutlu, Pelin; Özdemir, Muhittin Emre; Özdaş, Şule Beyhan; Kavaklı, İbrahim Halil; Türkay, Metin; Master Student; Master Student; Researcher; Faculty Member; Faculty Member; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 40319; 24956
    It has been shown that prostate cancer is associated with elevated androgen biosynthesis; therefore, inhibiting the activity of Cytochrome P450 17 (CYP17) may prevent progression of prostate cancer. In this study we identified, using in silico and experimental methods, two novel steroidal and non-steroidal lead compounds that inhibit the activity CYP17.
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    PublicationOpen Access
    Classification of drug molecules considering their IC(50) values using mixed-integer linear programming based hyper-boxes method
    (BioMed Central, 2008) Department of Industrial Engineering; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Armutlu, Pelin; Özdemir, Muhittin Emre; Yüksektepe, Fadime Üney; Kavaklı, İbrahim Halil; Türkay, Metin; Faculty Member; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; N/A; N/A; N/A; 40319; 24956
    Background: A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC(50) values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. Results: We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC(50) values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naive Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. Conclusion: Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.