Drug discovery studies on quinoline-based derivatives as potential antimalarial agents

Molecular modelling studies were performed to identify the essential structural requirements of quinoline-based derivatives for improving their antimalarial activity. The developed CoMFA, CoMSIA and HQSAR models for a training set comprising 37 derivatives showed good statistical significance in terms of internal cross validation (q²) 0.70, 0.69 and 0.80 and non-cross validation (r²) 0.80, 0.79 and 0.80. Also, the predicted r² values (r²_pred) of 0.63, 0.61 and 0.72 for a test set consisting of 12 compounds suggested significant predicting ability of the models. Structural features were correlated in terms of steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor interactions. Furthermore, the bioactive conformation was explored and explained by docking compounds #28, 32 and 40 into the active binding site of lactate dehydrogenase of Plasmodium falciparum. The QSAR models, contour map and docking binding affinity obtained could be successfully utilized as a guiding tool for the design and discovery of novel quinoline-based derivatives with potent antimalarial activity.     

Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach

Angiotensin-converting enzyme (ACE) inhibitors have been acknowledged as first-line agents for the treatment of hypertension and a variety of cardiovascular disorders. In this context, quantitative structure–activity relationship (QSAR) models for a series of non-peptide compounds as ACE inhibitors are developed based on Simplified Molecular Input-Line Entry System (SMILES) notation and local graph invariants. Three random splits into the training and test sets are used. The Monte Carlo method is applied for model development. Molecular docking studies are used for the final assessment of the developed QSAR model and the design of novel inhibitors. The statistical quality of the developed model is good. Molecular fragments responsible for the increase/decrease of the studied activity are calculated. The computer-aided design of new compounds, as potential ACE inhibitors, is presented. The predictive potential of the applied approach is tested, and the robustness of the model is proven using different methods. The results obtained from molecular docking studies are in excellent correlation with the results from QSAR studies. The presented study may be useful in the search for novel cardiovascular therapeutics based on ACE inhibition.     

3-D QSAR studies on histone deacetylase inhibitors. A GOLPE/GRID approach on different series of compounds

Docking simulation and three-dimensional quantitative structure-activity relationships (3D-QSARs) analyses were conducted on four series of HDAC inhibitors. The studies were performed using the GRID/GOLPE combination using structure-based alignment. Twelve 3-D QSAR models were derived and discussed. Compared to previous studies on similar inhibitors, the present 3-D QSAR investigation proved to be of higher statistical value, displaying for the best global model r2, q2, and cross-validated SDEP values of 0.94, 0.83, and 0.41, respectively. A comparison of the 3-D QSAR maps with the structural features of the binding site showed good correlation. The results of 3D-QSAR and docking studies validated each other and provided insight into the structural requirements for anti-HDAC activity. To our knowledge this is the first 3-D QSAR application on a broad molecular diversity training set of HDACIs.     

Molecular docking and QSAR of aplyronine A and analogues: potent inhibitors of actin

Actin-binding natural products have been identified as a potential basis for the design of cancer therapeutic agents. We report flexible docking and QSAR studies on aplyronine A analogues. Our findings show the macrolide ‘tail’ to be fundamental for the depolymerisation effect of actin-binding macrolides and that it is the tail which forms the initial interaction with the actin rather than the macrocycle, as previously believed. Docking energy scores for the compounds were highly correlated with actin depolymerisation activity. The 3D-QSAR models were predictive, with the best model giving a q ² value of 0.85 and a r ² of 0.94. Results from the docking simulations and the interpretation from QSAR “coeff*stdev” contour maps provide insight into the binding mechanism of each analogue and highlight key features that influence depolymerisation activity. The results herein may aid the design of a putative set of analogues that can help produce efficacious and tolerable anti-tumour agents. Finally, using the best QSAR model, we have also made genuine predictions for an independent set of recently reported aplyronine analogues.     

Prediction of anti-HIV activity and cytotoxicity of pyrimidinyl and triazinyl amines: A QSAR study

A QSAR study on a series of pyrimidinyl and triazinyl amines was performed to explore the physico-chemical parameters responsible for their anti-HIV activity and cytotoxicity. Physico-chemical parameters were calculated using WIN CAChe 6.1. Stepwise multiple linear regression analysis was carried out to derive QSAR models which were further evaluated for statistical significance and predictive power by internal and external validation. The selected best QSAR models showed correlation coefficient R of 0.914 and 0.901, and cross-validated squared correlation coefficient Q ² of 0.685 and 0.691 for anti-HIV activity and cytotoxicity, respectively. The developed significant QSAR model indicates that hydrophobicity of the whole molecule plays an important role in the anti-HIV activity and cytotoxicity of pyrimidinyl and triazinyl amine derivatives. When hydrophobicity is increased, anti-HIV activity of the present series of compounds is decreased leading to high cytotoxicity.     

CoMFA 3D-QSAR Analysis of Epothilones Based on Docking Conformation and Alignment

Epothilones belong to a class of novel microtubule stabilizing and anti-mitotic agents, which have a paclitaxel-like mechanism of action. A three-dimensional quantitative structure-activity relationship （3D-QSAR） model was built for epothilones by the method of comparative molecular field analysis （CoMFA） combined with the flexible docking technology. The docking CoMFA model gave a good cross-validated value of q2=0.784 with an optimized component of 6 and the conventional correlation coefficient of r^2=0.985. The statistical results show that the model has good ability to predict the activity of the studied compounds. At last, the docking CoMFA model was analyzed through contour maps complemented with MOLCAD-generated active site potential surface in the α,β-tubulin receptor, which can provide important information for the structure-based drug design.     

Binding conformations,QSAR, and molecular design of Alkene‐3‐quinolinecarbonitriles as Src inhibitors

A theoretical study on binding orientations and quantitative structure–activity relationship (QSAR) of a novel series of alkene‐3‐quinolinecarbonitriles acting as Src inhibitors has been carried out by using the docking study and three‐dimensional QSAR (3D‐QSAR) analyses. The appropriate binding orientations and conformations of these compounds interacting with Src kinase were revealed by the docking studies, and the established 3D‐QSAR models show significant statistical quality and satisfactory predictive ability, with high R² values and q² values: comparative molecular field analysis (CoMFA) model (q² = 0.748, R² = 0.972), comparative molecular similarity indices analysis (CoMSIA) model (q² = 0.731, R² = 0.987). The systemic external validation indicated that both CoMFA and CoMSIA models possessed high predictive powers with $ R{^2}_{\!\!\!\rm pred} $ values of 0.818 and 0.892, $ {r^2}_{\!\!\!\rm m} $ values of 0.879 and 0.886, $ {r^2}_{\!\!\!\rm m(LOO)} $ values of 0.874 and 0.874, $ r^2_{\rm m(overall)} $ values of 0.879 and 0.885, respectively. Several key structural features of the compounds responsible for inhibitory activity were discussed in detail. Based on these structural factors, eight new compounds with quite higher predicted Src‐inhibitory activities have been designed and presented. We hope these theoretical results can offer some valuable references for the pharmaceutical molecular design as well as the action mechanism analysis. © 2012 Wiley Periodicals, Inc.     

Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of <Emphasis Type="Italic">myasthenia gravis</Emphasis>

The Monte Carlo method was used for QSAR modeling of dimeric pyridinium compounds as acetylcholine esterase inhibitors. QSAR model was developed for a series of 39 dimeric pyridinium compounds. QSAR models were calculated with the representation of the molecular structure by the simplified molecular-input line-entry system. One split into the training and test set have been examined. The statistical quality of the developed model is very good. The calculated model for dimeric pyridinium derivatives had following statistical parameters: r ² = 0.9477 for the training set and r ² = 0.9332 the test set. Structural indicators considered as molecular fragments responsible for the increase and decrease in the inhibition activity have been defined. The computer-aided design of new dimeric pyridinium compounds potential acetylcholine esterase inhibitors with the application of defined structural alerts has been presented.     

Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors

Obesity is one of the most provoking health burdens in the developed countries. One of the strategies to prevent obesity is the inhibition of pancreatic lipase enzyme. The aim of this study was to build QSAR models for natural lipase inhibitors by using the Monte Carlo method. The molecular structures were represented by the simplified molecular input line entry system (SMILES) notation and molecular graphs. Three sets – training, calibration and test set of three splits – were examined and validated. Statistical quality of all the described models was very good. The best QSAR model showed the following statistical parameters: r² = 0.864 and Q² = 0.836 for the test set and r² = 0.824 and Q² = 0.819 for the validation set. Structural attributes for increasing and decreasing the activity (expressed as pIC₅₀) were also defined. Using defined structural attributes, the design of new potential lipase inhibitors is also presented. Additionally, a molecular docking study was performed for the determination of binding modes of designed molecules.     

QSAR of α-campholenic derivatives with sandalwood odor, and molecular design

Abstract  

Quantitative structure–activity relationship (QSAR) studies have been carried out in a series of α-campholenic derivatives with sandalwood odor based on quantum-chemical data derived by use of the Hartree–Fock (HF) method. To build QSAR models, a multiple linear regression method was used. The models obtained have good predictive ability and are of high statistical significance, with good correlation coefficients, and p values less than 0.05. The models contribute also to identification of important quantum-chemical aspects of the sandalwood odor. On the basis of the QSAR models built, several new sandalwood compounds were designed and the best candidate for experimental synthesis was suggested.