3.1: Ligand preparation:
A series of benzimidazole derivatives were used to prepare the ligand dataset using benzimidazole as the query molecule. All the inhibitors selected comply with the Lipinski rule of five. A total of 50 molecules were taken up for the study.
3.2 Virtual screening:
To further screen the compounds, additional similarity metrics were calculated by ROCS. ROCS is a shape based superposition method used to perceive similarity between molecules based on their three dimensional shape. We used benzimidazole as the query molecule. The extent of overlap between the ligands and the query was quantified in the form of a tanimoto combo score. The 3D overlap was based on matching the pharmacophoric features like the H-bond acceptor …show more content…
To predict the activity of our dataset, we have performed the field based QSAR analysis. A total of 55 benzimidazole derivatives with known anti-fungal and anti-protozoan activity were selected as reference standards. Field based QSAR is a tool for modeling the relationship between the known actives and the unknowns. The 3D characteristics of the compounds are matched with the known actives for activity prediction. 3D QSAR models are based on fields, such as electrostatic, hydrophobic or steric fields for a set of aligned ligands. The reference standards were divided into a training set comprising of 38 compounds and a test set having 17 compounds. The scatter diagram showing the predicted activity of the training and test is shown in Figure 2. Based on these datasets, the activity of our 35 derivatives was predicted. We selected ten compounds which showed relatively lower IC50 values and those that had a higher Tanimoto combo score. We also observed the 2D fingerprints of each of these ten compounds which provided information regarding the number of rotatable bonds, molecular weight, fragmental descriptors, physicochemical properties and topological indices such as branching index and the chi molecular connectivity indices. (Table 2 and
A series of benzimidazole derivatives were used to prepare the ligand dataset using benzimidazole as the query molecule. All the inhibitors selected comply with the Lipinski rule of five. A total of 50 molecules were taken up for the study.
3.2 Virtual screening:
To further screen the compounds, additional similarity metrics were calculated by ROCS. ROCS is a shape based superposition method used to perceive similarity between molecules based on their three dimensional shape. We used benzimidazole as the query molecule. The extent of overlap between the ligands and the query was quantified in the form of a tanimoto combo score. The 3D overlap was based on matching the pharmacophoric features like the H-bond acceptor …show more content…
To predict the activity of our dataset, we have performed the field based QSAR analysis. A total of 55 benzimidazole derivatives with known anti-fungal and anti-protozoan activity were selected as reference standards. Field based QSAR is a tool for modeling the relationship between the known actives and the unknowns. The 3D characteristics of the compounds are matched with the known actives for activity prediction. 3D QSAR models are based on fields, such as electrostatic, hydrophobic or steric fields for a set of aligned ligands. The reference standards were divided into a training set comprising of 38 compounds and a test set having 17 compounds. The scatter diagram showing the predicted activity of the training and test is shown in Figure 2. Based on these datasets, the activity of our 35 derivatives was predicted. We selected ten compounds which showed relatively lower IC50 values and those that had a higher Tanimoto combo score. We also observed the 2D fingerprints of each of these ten compounds which provided information regarding the number of rotatable bonds, molecular weight, fragmental descriptors, physicochemical properties and topological indices such as branching index and the chi molecular connectivity indices. (Table 2 and