Compound prioritisation and selection
In this demo we’re going to take a look at how StarDrop can guide the prioritisation and selections of compounds using a combination of in vitro and in silico data.
In addition to the StarDrop Models, it is possible to create your own models from your data with the Auto-Modeller module. After creating your models, they can be loaded into StarDrop so that their predictions can be calculated and displayed alongside the internal models as well as the Auto-Modeller models.
Details on how to build models with Auto-Modeller are described in Section 23 of the User Guide. For more information on how the models are built, see Section 8 of the Reference Guide.
You can access the guides within StarDrop or download them from our documentation page. Please note that you’ll need credentials to access them from our website
This worked example tutorial explores the application of the Auto-Modeller module. The tutorial shows how to build a QSAR model of potency against the Muscarinic Acetylcholine M5 receptor based on public domain Ki data. The resulting model is applied to novel compounds to predict their properties and visualise the SAR.
Check out these instructions on how to integrate your own models into StarDrop
For further information on any of the above, please contact us at support@optibrium.com.
More StarDrop resources
Python custom model code for StarDrop
This example code shows how to write a custom model using Python. This example can be used to make in-house…
HLM stability models
StarDrop™ HLM Stability Models are courtesy of Alexey Zakharov of the National Cancer Institute, National Institutes of Health, who has developed models of stability in Human liver Microsomes (HLM)