Pharmacophore Construction Using Discovery Studio
Introductions
Pharmacophore-based approaches can be considered an integral part of the modern computer-aided drug design toolbox. Pharmacophores are collections of spatial and electronic features that are necessary to ensure optimal molecular interactions with a specific biological target structure and to trigger (or block) its biological response. Pharmacophores provide a very useful tool to represent the nature of molecules involved in ligand-target receptor interactions as well as the location of functional groups, and all types of non-covalent interactions can be represented as geometric entities. In addition to the abstract characterization of known structures, pharmacophore modeling can help to design new molecules and predict their activity.
Tutorials
- Training set molecular preparation
First, we need to discover the chemical characteristics shared by a series of active ligand small molecules with the help of Discovery Studio software, and then automatically generate the pharmacophore model based on the comparison and superposition of these common characteristics.
Click View>Explorers to check ✔ in front of File to open the file browser, click Samples>Tutorials>Pharmacophore, double click 1A52_ligands.sd, at this time the six active molecules of receptor 1A52 are opened.
We need to define the Principal and MaxOmitFeat attributes of the small molecules, select the remaining column of the heading in the table browser, right mouse click and select Add Attributes to open the Add Attributes dialog box, add these two attributes with three values of 0, 1 and 2 for each small molecule with different attributes The Principal attribute defines the activity of the small molecule.
The Principal attribute defines the activity level of the small molecule, with 2 being active, 1 being moderately active, and 0 being inactive.
- Selection of the characteristic elements of the pharmacophore
In the display window, right-click and select Show All to display the structures of all active compounds. In the toolbar, click Pharmacophore>Edit and Cluster Features>Feature Mapping to open the Feature Mapping dialog box and select 1A52_ligands:All for Input Ligands.
Click on the Features right side... button to open the Select Features dialog box, select the feature elements to be matched, set the default value here. Click Run to run.
Expand the Conformation Generation parameter group, click the grid to the right of Conformation Generation, select BEST, set Maximum Conformation to 200, set Energy Threshold to 10, and set other parameters to default.
Click Run to run the task.
Result Analysis
After the task is completed, the Common Feature Pharmacophore Generation window will pop up automatically, or you can click Report in the Job Browser to view it.
From the results, we can see that this task has generated 10 pharmacophore models, which are ranked according to the matching degree between the molecules in the training set and the pharmacophore model and the rarity of the model itself.
The ranking order does not mean that the models are good or bad, we need to analyze these 10 models to select the best model, and even manually optimize the pharmacophore features to obtain the most reliable model.
Details column description
Go back to the Report page and expand the Details column. The table lists the resulting parameters for the ten pharmacophore models.
Each row in the table represents a pharmacophore, where:
a. Features indicate the characteristics in the pharmacophore model, R represents the aromatic ring center, P represents the positively charged ion center, H represents the hydrophobic characteristics, A represents the hydrogen bond acceptor characteristics, and D represents the hydrogen bond donor characteristics.
b. Rank indicates the scoring value of the pharmacophore, the higher the score, the better.
c. Direct Hit indicates the match between the pharmacophore and the training set molecule, 1 means match, 0 means no match, e.g. 111111 means the pharmacophore can match with 6 small molecules.
d. Partial Hit indicates the number of pharmacophore features that are partially matched with the molecules in the training set.
e. Max Fit indicates the matching of the pharmacophore features, e.g. 6 means that all 6 pharmacophore features can be matched.
Related Services
Pharmacophore Model Construction Service
Ligand-based Pharmacophore Model Service
Receptor-based Pharmacophore Model Service
* For Research Use Only.
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