Development, evaluation and application of 3D QSAR Pharmacophore model in the discovery of potential human renin inhibitors

Abstract

BackgroundRenin has become an attractive target in controlling hypertension because of the high specificity towards its only substrate, angiotensinogen. The conversion of angiotensinogen to angiotensin I is the first and rate-limiting step of renin-angiotensin system and thus designing inhibitors to block this step is focused in this study.MethodsLigand-based quantitative pharmacophore modeling methodology was used in identifying the important molecular chemical features present in the set of already known active compounds and the missing features from the set of inactive compounds. A training set containing 18 compounds including active and inactive compounds with a substantial degree of diversity was used in developing the pharmacophore models. A test set containing 93 compounds, Fischer randomization, and leave-one-out methods were used in the validation of the pharmacophore model. Database screening was performed using the best pharmacophore model as a 3D structural query. Molecular docking and density functional theory calculations were used to select the hit compounds with strong molecular interactions and favorable electronic features.ResultsThe best quantitative pharmacophore model selected was made of one hydrophobic, one hydrogen bond donor, and two hydrogen bond acceptor features with high a correlation value of 0.944. Upon validation using an external test set of 93 compounds, Fischer randomization, and leave-one-out methods, this model was used in database screening to identify chemical compounds containing the identified pharmacophoric features. Molecular docking and density functional theory studies have confirmed that the identified hits possess the essential binding characteristics and electronic properties of potent inhibitors.ConclusionA quantitative pharmacophore model of predictive ability was developed with essential molecular features of a potent renin inhibitor. Using this pharmacophore model, two potential inhibitory leads were identified to be used in designing novel and future renin inhibitors as antihypertensive drugs.

Tubulin inhibitors: pharmacophore modeling, virtual screening and molecular docking

“…This method generates pharmacophore hypotheses by www.chinaphar.com Niu MM et al Acta Pharmacologica Sinica npg randomizing the activity data of these compounds while using the same parameters and features used to generate the original pharmacophore hypothesis. For the Fischer's randomization test, a 95% confidence level was chosen for this validation study, and 19 random spreadsheets were constructed [19,20] . During the pharmacophore generation process, if the randomized data set generates similar or better cost values, RMSD and correlation, the original hypothesis were generated by chance [21] .…”

“…The cost difference between the null and fixed cost values should be larger for a significant pharmacophore model. A value of 40-60 bits in a model implies that it has 75%-90% probability of representing a true correlation within the data [19,20] . The hypotheses are also evaluated based on other cost components.…”

“…Hydrogen bond donor (HBD), hydrogen bond acceptor (HBA), hydrophobic (HY) and ring aromatic (RA) features were used to generate ten pharmacophore models. All other parameters used in the HypoGen module were kept at their default settings [19,20] . In this study, the top 10 hypothetical structures returned by the generation process were selected for further calculations.…”

3D-QSAR Study for Checkpoint Kinase 2 Inhibitors through Pharmacophore Hypotheses

“…Pharmacophore [16]- [20], [30]- [32] is a set of structural features responsible for its biological activity of a molecule. It allowed compounds with diverse structures to find the common chemical features by ligand pharmacophore mapping, and that is different from CoMFA and CoMSIA.…”

“…It is used to correlate the structure properties of compounds with their biological activities. The method to predict the quality by QSAR was improved by considering the three-dimensional structure QSAR (3D-QSAR) [15]- [20] of targeted inhibitor. Therefore, the compound structure can be directly optimized in the 3D space.…”