The objective in this example is to identify one or more high quality chemistries for progression to detailed in vitro and in vivo studies, based on initial screening data for potency; ideally the compounds chosen for progression should not only be potent, but also have appropriate ADME properties to result in a high quality lead series. We will also use StarDrop to explore potential modification of one of the existing compounds to improve its properties.
Please note that the tutorial explores core features of StarDrop and the ADME QSAR module. If you don’t have access to this module and would like to arrange a free license, please email us at info@optibrium.com
In this example we will illustrate how knowledge-based predictions of toxicity can be used within a MPO environment to guide the selection and design of compounds with a good balance of properties and reduced risk of toxicity.
This study aimed to create a model for predicting pKa using a semi-empirical quantum mechanics (QM) approach combined with machine learning (ML).
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