With its highly visual, easy-to-use interface,
StarDrop guides you through each stage of the compound selection and design process; in guiding your decisions, its virtual environment directs you to a successful outcome
A modular suite of software with a range of plug-ins...
 | Predict key ADME and physicochemical properties prior to synthesis 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 and physiochemical properties in silico. |
 | Quantum mechanical simulation of drug metabolism Using StarDrop's P450 metabolism models you can quickly identify the regions of your molecules that are most vulnerable to metabolism by the major drug metabolising isoforms of cytochrome P450. |
 | Automatic generation of chemistry ideas - Rigourously explore the chemistry around hits - Discover lead-hopping opportunities - Generate new strategies to overcome liabilities - Identify patent-busting possibilities |
 | Analyse data and produce predictive models This module gives novice and expert users alike access to the tools needed to produce validated, predictive models. Output from the Auto-Modeller includes Glowing Molecule results. |
ADME QSAR Module
The ADME QSAR module enables you to predict a broad range of ADME and physicochemical properties, prior to synthesis, using a suite of high-quality models, including:
|
|
P450 Module
The P450 metabolism models identify the regions of a candidate molecule most vulnerable to metabolism by the three major cytochrome P450 enzymes. These enzymes account for around 95% of human Phase I metabolism. Using StarDrop's P450 metabolism models, molecules can be redesigned to overcome possible metabolism problems or drug-drug interactions. Combining these models with other factors such as the molecule's affinity or lipophilicity can demonstrate the risk of high metabolic turnover.
Auto-Modeller Module
The Auto-Modeler gives both novice and expert users access to the tools needed to thoroughly analyze data and produce validated, predictive models. Output from the Auto-Modeler includes Glowing Molecule. For non-expert users, the Auto-Modeler can:
- Automatically generate models: multiple advanced modelling techniques, including Gaussian Processes, Radial Basis Functions, PLS and Decision Trees can be applied to data, helping you identify the most appropriate model
- Train, test and validate: automatically split your data into sub-sets to rigorously choose and validate your models
- Generate descriptors: MWt, logP, polar surface area and many other 2D structural descriptors are included with the Auto-Modeler
Nova Module
Nova helps you generate new compound ideas, expand your searches, develop new chemistry strategies and find ways around patents by opening up a world of opportunities in the hunt for those elusive, high-quality compounds you may have overlooked or simply not thought of.
Nova exponentially broadens your search by taking a 'parent' molecule and creating new generations of related compounds. Using a built-in collection of typical 'medicinal chemistry' transformations, Nova explores generations of potential 'children'. Nova provides an initial library of over 200 transformations for you to apply. Furthermore, using Nova you can apply multiple generations of transformations and bias selection in favour of a property or score.
