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Abstract
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Article Information:
Retrieval Performance using Different Type of Similarity Coefficient for Virtual Screening
Shereena Arif, Noor Zeemah Shamsheh Khan, Nurul Malim and Suhaila Zainudin
Corresponding Author: Shereena Arif
Submitted: September 22, 2014
Accepted: October 24, 2014
Published: February 15, 2015 |
Abstract:
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Development of a new drug needs chemical databases as references to find lead compounds. This study aims to determine the best similarity coefficient to be used for virtual screening task using chemical databases. We calculated the structural resemblance between each pair of chemical structures in their own activity class to get the Mean Pairwise Similarity (MPS) value to see the nature of heterogeneity for each natural product and synthetic chemical databases. The process involves the 2D descriptor of type ECFC4 fingerprint to represent each structure and Tanimoto coefficient to calculate the similarity score between each pair of chemical structures in the same activity class. MPS for an activity class was obtained by taking the average of all similarity scores within that class. Next, three types of similarity coefficients have been used to calculate the similarity score between a query structure and each of the database structure. The results indicate that Tanimoto coefficient shows better performance compared to Russell Rao and Forbes in retrieval task using chemical database. This implies that Tanimoto coefficient is recommended to carry out virtual screening in drug development. More work should be carried out to determine the best combination of similarity coefficient and fingerprint type to get optimal retrieval performance.
Key words: Chemoinformatics, mean pairwise similarity, retrieval, similarity search, virtual screening, ,
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Cite this Reference:
Shereena Arif, Noor Zeemah Shamsheh Khan, Nurul Malim and Suhaila Zainudin, . Retrieval Performance using Different Type of Similarity Coefficient for Virtual Screening. Research Journal of Applied Sciences, Engineering and Technology, (5): 391-395.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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