Drug Res (Stuttg) 2019; 69(03): 159-167
DOI: 10.1055/a-0652-5290
Original Article
© Georg Thieme Verlag KG Stuttgart · New York

QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method

Parvin Kumar
1   Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
,
Ashwani Kumar
2   Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India
,
Jayant Sindhu
3   K.M. Govt. College, Narwana, Haryana, India
,
Sohan Lal
1   Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
› Author Affiliations
Further Information

Publication History

received 31 May 2018

accepted 01 July 2018

Publication Date:
23 July 2018 (online)

Abstract

Human farnesyl pyrophosphate synthase (hFPPS) is a well-settled therapeutic target and it is an enzyme of the mevalonate pathway which catalyzes the biosynthesis of the C-15 isoprenoid farnesyl pyrophosphate. QSAR studies by using Monte Carlo method for human farnesyl pyrophosphate synthase inhibitors has been carried out using balance of correlation technique with Index of ideality correlation. For construction of QSAR models, six random splits were prepared from the data of 73 phosphonates and hybrid optimal descriptors procured from graph (HFG) and SMILES based notations were employed. The developed QSAR models have robustness, good fitting ability, generalizability and internal predictive ability. The external predictive ability has been certified by testing various precedents. The values of R2, IIC, Q2 and ∆R2 m for the best model are 0.9304, 0.9614, 0.9061 and 0.0861 respectively. The developed QSAR models met with the specified standards given in OECD guideline and applicability domain. The structural feature promoters for the end point increase and promoters for end point decrease have been extracted. The predicted pIC50 for the new proposed compounds have also been reported.

Supporting Information

 
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