Optimization of antiproliferative activity of substituted phenyl 4-(2-oxoimidazolidin-1-yl) benzenesulfonates: QSAR and CoMFA analyses.

Multiple separate quantitative structure-activity relationships (QSARs) models were built for the antiproliferative activity of substituted Phenyl 4-(2-Oxoimidazolidin-1-yl)-benzenesulfonates (PIB-SOs). A variety of descriptors were considered for PIB-SOs through QSAR model building. Genetic algorithm (GA), available in QSARINS, was employed to select optimum number and set of descriptors to build the multi-linear regression equations for a dataset of PIB-SOs. The best three parametric models were subjected to thorough internal and external validation along with Y-randomization using QSARINS, according to the OECD principles for QSAR model validation. The models were found to be statistically robust with high external predictivity. The best three parametric model, based on steric, 3D- and finger print descriptors, was found to have R(2)=0.91, R(2)ex=0.89, and CCCex=0.94. The CoMFA model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, gave F=229.34, R(2)CV=0.71 and R(2)=0.94. Steric repulsion, frequency of occurrence of carbon and nitrogen at topological distance of seven, and internal electronic environment of the molecule were found to have correlation with the anti-tumor activity of PIB-SOs.

Synthesis of α, ß-unsaturated carbonyl based compounds as acetylcholinesterase and butyrylcholinesterase inhibitors: characterization, molecular modeling, QSAR studies and effect against amyloid ß-induced cytotoxicity.

A series of novel carbonyl compounds was synthesized by a simple, eco-friendly and efficient method. These compounds were screened for anti-oxidant activity, in vitro cytotoxicity and for inhibitory activity for acetylcholinesterase and butyrylcholinesterase. The effect of these compounds against amyloid ß-induced cytotoxicity was also investigated. Among them, compound 14 exhibited strong free radical scavenging activity (18.39 µM) while six compounds (1, 3, 4, 13, 14, and 19) were found to be the most protective against Aß-induced neuronal cell death in PC12 cells. Compounds 4 and 14, containing N-methyl-4-piperidone linker, showed high acetylcholinesterase inhibitory activity as compared to reference drug donepezil. Molecular docking and QSAR (Quantitative Structure-Activity Relationship) studies were also carried out to determine the structural features that are responsible for the acetylcholinesterase and butyrylcholinesterase inhibitory activity.

QSAR models for anti-malarial activity of 4-aminoquinolines.

In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six.

Optimization of antimalarial activity of synthetic prodiginines: QSAR, GUSAR, and CoMFA analyses.

In the present study, we have carried out extensive General Unrestricted Structure-Activity Relationships, conventional 3D-Quantitative Structure-Activity Relationships, and CoMFA analyses of synthetic prodiginines displaying moderate to high activities against Plasmodium Falciperum. 2D and 3D descriptors, various statistical parameters viz. R(2), R(2)(adj), standard error, Y-randomization, etc., were checked to build fruitful 3D-Quantitative Structure-Activity Relationships model. The best five parametric 3D-Quantitative Structure-Activity Relationships model is with R(2) = 0.924 and R(2)(pred) = 0.901. CoMFA was performed to check the electrostatic and steric regions, which affect the activity. The CoMFA model is graphically inferred using contour plots, which provide insight into the structural requirements for increasing the activity of a compound. The General Unrestricted Structure-Activity Relationships model, with R(2) = 0.940 and Q(2) = 0.912, suggests that the presence of F on aromatic ring is good for activity. The analyses reveal that lipophilicity plays a crucial role in deciding the activity for these molecules.

CoMSIA and POM analyses of anti-malarial activity of synthetic prodiginines.

In present work, 53 synthetic prodiginines were selected to establish thriving CoMSIA (Comparative Molecular Similarity Indices Analysis) model to explore the structural features influencing their anti-malarial activity. POM (Petra/Osiris/Molinspiration) was carried out to get insight into requirements that can lead to the improvement of the activity of these molecules. The CoMSIA model, based on a combination of steric, electrostatic and H-bond acceptor/donor effects, is with R(2)(cv)=0.738 and R(2)=0.911. The analyses reveal that lipophilicity, hydrogen donor/acceptor and steric factors play crucial role. The study with constructive propositions could be useful for the design of new analogues with enhanced activity.