Watch this video to gain an impression about GUSAR’s look-and-feel:
Estimation which parts of a molecule provide positive and negative impact to the activity
Saving GUSAR output predictions in SDF and CSV formats for subsequent analyses
Automatic, simplified creation of (Q)SAR models
Enhanced choice of variables:
· QNA descriptors
· MNA based descriptors
· Combinations between QNA, MNA descriptors and additional variables –
· Topological length of a molecule
· Topological volume of a molecule
· Calculated lipophilicity
Improved algorithm of model building and prediction:
· Self-consistent regression (SCR)
· Radial basis function (RBF) neural networks regression
· Both types of regression
· Nearest neighbors’ prediction correction
· Consensus of multiple models’ construction
· Different methods for model validation.
GUSAR is a tool for analysis of quantitative and qualitative structure-activity/structure-property relationships ((Q)SAR/(Q)SPR) on the basis of the structural formulas of the compounds and data on their activity/property, and for prediction of activity/property for new compounds. The acronym stands for “General Unrestricted Structure-Activity Relationships”. GUSAR can be easily applied to different routine (Q)SAR tasks, for building many models, and for prediction by these models of the different quantitative/qualitative values simultaneously.
The GUSAR software provides the following functions:
– reading of SD files with data on chemical structures and their activities/properties;
– creation and validation of (Q)SAR models;
– prediction of the activities/properties for the new compounds by created activities/properties (Q)SAR models and saving the prediction results in SD or CSV files.
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.
The GUSAR software optionally includes ready-trained GUSAR models (SAR bases) for predicting certain biological activities. These are SAR bases that can be used with the GUSAR program for predictions on acute rat toxicity, acute mouse toxicity or antitargets (off-targets).
The acute rat or mouse toxicity SAR bases can be used for in silico prediction of LD50 values for rats or mouse with four types of administration.
A quantitative prediction of antitarget interaction for chemical compounds can be done with the other SAR base. The QSAR models for the set of 32 activities (using IC50, Ki or Kact values) includes data on about 4,000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes and 3 transporters).
Click here to learn more.
