High quality predictive QSAR models of key ADME properties
Before committing resources to in vivo and in vitro studies, it’s vital to know you’re working with the best molecules. StarDrop is built to help you predict a wide range of ADME models and physicochemical properties in silico, allowing you to explore all your options and giving you confidence in your decisions – and all with an intuitive, interactive graphical interface.
This module enables you to predict a broad range of ADME and physicochemical properties using a suite of high-quality QSAR models including:
Independent test sets are used to validate every model.
Every prediction is accompanied by an estimate of confidence, helping you to fully consider uncertainties.
The applicable chemical space is captured for each model. Molecules outside this space – where confidence in the predictions are lower – are clearly identified.
StarDrop interfaces with in-house models to supplement or replace StarDrop’s optional models. StarDrop models can also be run from other informatics platforms.
The StarDrop ADME QSAR models provide Glowing Molecule results.
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.
The ADME QSAR is available as optional module for StarDrop. To trial StarDrop please complete the form and a member of the team will get in touch to understand your needs and get you set-up with a licence that works best for you.
WITH A RANGE OF
StarDrop's™ core features can be extended with a comprehensive range of optional plug-in modules; predictive models for ADME properties, Phase I and II metabolic routes and toxicity; automatic QSAR model building; 3D SAR analysis; and de novo design to stimulate the search for new optimisation strategies.