Build and validate robust QSAR models tailored to your chemistry and data
This module gives both novice and expert users access to the tools needed to produce validated, predictive models. Output from the Auto-Modeller includes Glowing Molecule results that help to visualise the structure-activity relationships captured from your chemistry and data.
Even if you are not an expert in modelling you can:
- Automatically generate predictive models
- Use multiple advanced modelling techniques, including Gaussian Processes, Random Forests, Radial Basis Functions, PLS and Decision Trees
- Train, test and validate: automatically split your data into sub-sets to rigorously choose and validate the best model
- Use a suite of built-in descriptors; MWt, logP, polar surface area and many other 2D structural descriptors are included with the Auto-Modeller
As an expert user you can also manually tune modelling methods, define your own data set splits and descriptors and access detailed analyses of the results.
If you would like to know more about the Auto-Modeler, take a look at the FAQs.
The Auto-Modeller is available as an optional module for StarDrop. To find out more about StarDrop and the Auto-Modeller, to arrange an online demonstration or to try StarDrop for yourself, please contact us.
Take a look at our ACS presentation where we describe the Gaussian Processes method, discuss its strengths and weaknesses and compare its results with other QSAR modelling methods. This is illustrated by several examples of applications to different QSAR modelling problems.
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.