A high quality drug must exhibit a balance of many properties, including potency, ADME and safety. In drug discovery this is particularly challenging due to complex, often conflicting property requirements combined with uncertain data because of experimental variability or predictive error.
StarDrop’s unique probabilistic scoring enables you carry out multi-parameter optimisation (MPO), enabling you to identify those compounds that have the best balance of those properties necessary for success in your projects. You can define the weighted profile of property criteria for your project and compare all your candidate molecules against these requirements to quickly see which are most and least likely to succeed.
Confidence in each score is calculated to highlight statistically significant compounds
Allow for trade-offs between properties for your particular project.
Including StarDrop’s ADME QSAR or Metabolism modules, toxicity alerts from the Derek Nexus module or imported in vitro, in vivo or in silico data.
Profiles can be customisable for any project within an intuitive interface.
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 peer-reviewed article, published in the Journal of Medicinal Chemistry, describes how identifying sensitive criteria can highlight new avenues for exploration, and assist us in avoiding missed opportunities
Summary In this drug optimisation article, co-authored with Pfizer we discuss new ‘rule induction’ methods. These explore complex data to…
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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 discovery process.