Cp-MLR Directed Qsar Rationales for the Activity Profile of (Phenylpiperazinyl-Alkyl) Oxindoles as 5-Ht7 Receptor Ligands

Abstract

The 5-HT7 receptor binding affinities of the oxindole derivatives have been quantitatively analyzed in terms of Dragon descriptors. The derived QSAR models have provided rationales to explain the binding affinity of titled compounds. In order to improve the 5-HT7 receptor binding affinity of a compound higher value of molecular topology and symmetry accounting parameter path/walk 2-Randic shape index (descriptor PW2) and lower value of eigenvector coefficient sum from adjacency matrix (descriptor VEA1) are favorable. Presence of more number of sp 3 hybridized secondary carbon atoms (descriptor nCs) and secondary aliphatic amides (descriptor nCONHR) in a molecule will be supportive to the activity. The associations of masses to the eigenvalue n.4 of Burden matrix (BEHm4), van der Waals volume to path length 2 of Geary autocorrelation (GATS2v) and Sanderson electronegativity to path length 6 of Moran autocorrelation (MATS6e) and path length 4 of Geary autocorrelation (GATS4e) have shown the prevalence of atomic properties and charge content in terms of 9 th and 5 th order topological and mean topological charge indices (GGI9 and JGI5) to explain the binding affinity. The PLS analysis has also confirmed the dominance of information content of the CP-MLR identified descriptors. The derived models and participating descriptors in them have suggested that the substituents of oxindole moiety have sufficient scope for further modification.

Structural Requirement of PPARα Agonist Through QSARs and Molecular Simulation Analyses

“…Importance of the fragment is evident as such fragment is absent in the active compounds (Cpds 1 and 2, value of this descriptor = 0), but significantly present in inactive compounds (Cpds 19 and 20). The descriptor nCONHR denotes the number of secondary aliphatic amides (Choudhary & Sharma, 2015), which also has a negative contribution towards the activity. Some active molecules (Cpds 1 and 2) do not have secondary aliphatic amide group but H-atom attached with the α carbon makes them most active compounds of the dataset.…”

“…It is seen that an electotopological index, S_aaCH (Sum of aaCH E-states) (Todeschini & Consonni, 2009) and an atom type descriptor, Atype_C_5 (CH3X, where X is either heteroatom -O, N, S, P, Se or halogen) (Choudhary & Sharma, 2015), and two fuctional group count descriptors, nCONHRPh (number of secondary aromatic amides) and nROR (number of aliphatic ethers) have the positive contributions towards the activity, whereas the descriptors S_aaSN (Sum of aasN E-states) and Jusr_RNCS (the solvent-accessible surface area of the most negative atom obtained by the relative negative charge of that molecule)) and another atom type indices Atype_C_25 (-CR3, the tertiary C-atom, where R is any group linked through carbon) have equal distributions towards positive and negative activity. So, the model concludes the number of secondary aromatic amides and aliphatic ether in a compound have positive influence on the PPARα activity, which is depicted in the active compounds (Cpd 42 and Cpd 45).…”