2D structure alignment
Using the 2D structure alignment tool in StarDrop, define a substructure to perform a rigid alignment of molecules in the data set.
These two models calculates the number of sp3 carbons and the total number of carbons compound. These are available to enable the calculation of properties such as saturation – the ratio of sp3 carbons to the total number of carbons (Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 2009, 52:6752–6756.). They suggest that the typical levels of saturation increase from 0.36 in drug discovery through to 0.47 in drugs.
When installed, each will appear in StarDrop in the “Models” tab alongside the ADME QSAR models and other simple properties as a “Custom” model, allowing it to be calculated easily for any data set.
To calculate the saturation, run these models and use the StarDrop function editor to create a new column which calculates:
{Number of sp3 carbons}/{Number of carbons}
To access the models quickly you can simply right-click on the Models tab in StarDrop, choose Open Model… and then open the saved files.
To ensure StarDrop always has these models available, open the Preferences (File->Preferences) and, in the File Locations tab, add the directory where the models were saved into the Models section.
To access the models quickly you can simply right-click on the Models tab in StarDrop, choose Open Model… and then open the saved files.
To ensure StarDrop always has these models available, open the Preferences (File->Preferences) and, in the File Locations tab, add the directory where the models were saved into the Models section.
Using the 2D structure alignment tool in StarDrop, define a substructure to perform a rigid alignment of molecules in the data set.
This script provides a method for calculating Ligand Efficiency in StarDrop; a simple quantifiable metric for assessing whether a ligand derives…
This study aimed to create a model for predicting pKa using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML).