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J Recept Signal Transduct Res. 2016;36(2):189-206. doi: 10.3109/10799893.2015.1075040. Epub 2015 Sep 29.

Qualitative and quantitative pharmacophore-similarity assessment of anthranilamide-based factor Xa inhibitors: applications on similar molecules with identical biological endpoints.

Author information

1
a Department of Bioinformatics , Applied Botany Centre (ABC), Gujarat University , Ahmedabad , Gujarat , India and.
2
b Division of Medicinal Chemistry and Pharmacogenomics, Department of Cancer Biology , The Gujarat Cancer and Research Institute , Ahmedabad , Gujarat , India.

Abstract

It is a conventional practice to exclude molecules with identical biological endpoints to avoid bias in the resulting hypothesis model. Despite the diverse chemical functionalities, the receptor interactions of such molecules are often unexplored. The present study motivates the selection of these molecules diversified by single atom or functional group compared to internal molecules as external set and helps in the understanding of corresponding effects toward receptor interactions and biological endpoints. Applied on anthranilamide-series of factor Xa analogs, the inhibitory activities were correlated (r(2) = 0.99) and validated (q(2) = 0.68) with distance-based pharmacophore descriptors using support vector machine. The selected external set molecules exhibited better prediction accuracy by securing activities less than one residual threshold. The effect on inhibitory activity was assessed by the examination of pharmacophore-similarity and its interactions with key residues of Human factor Xa enzyme using molecular docking approach. Furthermore, qualitative pharmacophore models were developed on the subset of molecular dataset divided as most actives, moderately actives and least actives, to recognize crucial activity governing pharmacophore features. The outcome of this study will bring new insights about the requirements of pharmacophore features and prioritizes its selection in the design and optimization of potent Xa inhibitors.

KEYWORDS:

Anthranilamide; distance-based pharmacophore descriptors; human factor Xa enzyme; pharmacophore modeling; support vector machine

PMID:
26416308
DOI:
10.3109/10799893.2015.1075040
[Indexed for MEDLINE]

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