CP-MLR/PLS directed quantitative structure-activity relationship study on the histamine H3 receptor binding affinity: The cyclohexylamine based series
Department of Chemistry, Government College, Bundi-323 001, Rajasthan, India.
Research Article
World Journal of Biology Pharmacy and Health Sciences, 2023, 13(02), 089-104.
Article DOI: 10.30574/wjbphs.2023.13.2.0074
Publication history:
Received on 28 December 2022; revised on 01 February2023; accepted on 04 February 2023
Abstract:
The histamine H3 receptor binding affinities of cyclohexylamine derivatives has been analysed with the topological and molecular features from Dragon software. Analysis of the structural features in conjunction with the biological endpoints in combinatorial protocol in multiple linear regression (CP-MLR) led to the identification of 26 descriptors for modelling the activity. The study clearly suggested the role of atomic properties such as mass, electronegativity or charge content, polarizability, atomic van der Waals volume, average valence connectivity index chi-5 and absence of number of acceptor atoms for H-bonds (N, O, F) type functionality to optimise the histamine H3 receptor binding affinity of titled compounds. The models developed and the participating descriptors advocate that the substituent groups of the cylohexylamine moiety hold scope for further modification in the optimization of the H3 receptor binding affinity.
Analysis of these descriptors in partial least squares (PLS) highlighted their relative significance in modulating the biological response. The selected descriptors are enriched with information corresponding to the activity when compared to the remaining ones. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.
Keywords:
QSAR; Histamine H3 receptor; Binding affinity; Combinatorial protocol in multiple linear regression (CP-MLR) analysis; PLS; Dragon descriptors; Cyclohexylamine derivatives
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