Researcher:
Kahraman, Pınar

Profile Picture
ORCID

Job Title

Master Student

First Name

Pınar

Last Name

Kahraman

Name

Name Variants

Kahraman, Pınar

Email Address

Birth Date

Filters

20072

Advanced Search

Filter by

Settings

Researcher Of Publications: search.filters.isAuthorOfPublication.5cfa0435-e53c-447a-a8a6-6d0c36275731

Search Results

Now showing 1 - 2 of 2
  • Placeholder
    PublicationMetadata 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; 24956
    The 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.
  • Placeholder
    PublicationMetadata 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/A
    The 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.