Addressing Toxicity Risk in Multi-Parameter Optimisation
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. We will explore a library of compounds with COX2 inhibition data, with the goal of identifying a high quality lead series, using StarDrop’s Probabilistic Scoring to integrate experimental data, predicted ADME properties from the ADME QSAR module and predictions of toxicity risk from the Derek Nexus module.
Please note that the tutorial explores core features of StarDrop and the Derek Nexus and ADME QSAR modules. If you don’t have access to these modules and would like to arrange a free trial license, please email us at [email protected]
Please note that this video was recorded with an earlier version of StarDrop, so there may be small differences in appearance, however the process demonstrated is unchanged.
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With its comprehensive suite of integrated software, StarDrop™ delivers best-in-class in silico technologies within a highly visual and user-friendly interface. StarDrop™ enables a seamless flow from the latest data through predictive modelling to decision-making regarding the next round of synthesis and research, improving the speed, efficiency, and productivity of the drug optimisation and discovery process.