Competitive advantages and peculiarities

The program allows creating individual (Q)SAR models and their sets, presented in the form of consensus as well.

It can be used for the creation of (Q)SAR models for the prediction of properties of organic compounds belonging to both homogeneous and heterogeneous chemical classes.

It is based on unique and innovative atom centric descriptors which are called Quantitative and Multilevel Neighborhoods of Atoms (QNA and MNA) descriptors.

It uses modern and robust machine learning approaches: self-consistent regression and radial basis functions for automatic creation of (Q)SAR models. 

Along with prediction results the end-user can get an evaluation of applicability domain of the model.

Visualization of contributions of atoms into predicted value of the activity allows identifying the atoms and molecular fragments that make a positive and a negative contribution to the activity.

User-friendly interface, fast speed of the (Q)SAR models creation and prediction of the test compounds as well.