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Molecules 2016, 21(4), 476;

Adapting Document Similarity Measures for Ligand-Based Virtual Screening

Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor 81310, Malaysia
Faculty of Engineering, Karary University, Khartoum 12304, Sudan
Author to whom correspondence should be addressed.
Academic Editor: Derek J. McPhee
Received: 27 February 2016 / Revised: 31 March 2016 / Accepted: 6 April 2016 / Published: 13 April 2016
(This article belongs to the Section Molecular Diversity)
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Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cut-offs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods. View Full-Text
Keywords: chemoinformatics; similarly search; similarity coefficients; virtual screening; drug discovery chemoinformatics; similarly search; similarity coefficients; virtual screening; drug discovery

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Himmat, M.; Salim, N.; Al-Dabbagh, M.M.; Saeed, F.; Ahmed, A. Adapting Document Similarity Measures for Ligand-Based Virtual Screening. Molecules 2016, 21, 476.

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