StarDrop Features

Probabilistic Scoring

Multi-Parameter Optimisation

Rigorously assess your compounds’ potential for success against your project objectives using Probabilistic Scoring.

Unique Probabilistic Scoring

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.

Allow for uncertainty: confidence in each score is calculated to highlight statistically significant compounds

Weighted properties: allow for trade-offs between properties for your particular project

Use data from any source: StarDrop ADME QSAR or P450 models, toxicity alerts from the Derek Nexus module or imported in vitro, in vivo or in silico data

Multi-parameter optimisation: customisable for any project within an intuitive interface

For an overview of the range of methods used for multi-parameter optimisation (MPO) in drug discovery take a look as this preprint article written for a special issue of Current Pharamaceutical Design.

To find out more about how probabilistic scoring works, take a look at the FAQs.

To learn more about StarDrop and probabilistic scoring, or to arrange an online demonstration and perhaps try StarDrop for yourself, please contact us.

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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.