What is GUSAR about?

GUSAR is a tool to create models on quantitative structure-activity relationships. The acronym stands for “General Unrestricted Structure-Activity Relationships”.The input of the program is your training set of chemical structures and quantitative data on biological activities. The output is a reliable quantitative SAR/SPR (Structure Activity and Property Relationship) model.

Get a picture of GUSAR

Quantitative prediction of the activity of a chemical compound and its correlation with the known effect with GUSAR (right). The program also assigns to individual atoms whether they are supportive (green) and or suppressive (red) for the effect under consideration (left).
Quantitative prediction of the effect of a chemical compound. Click picture for enlarged view.

Watch this video to gain an impression about GUSAR’s look-and-feel:

Key features

Unique descriptors and mathematical algorithms
High speed of predictions
Easy-to-use interface
Selection of the most predictive models
Activity impact

Estimation which parts of a molecule provide positive and negative impact to the activity

Output extraction in SDF and CSV

Saving GUSAR output predictions in SDF and CSV formats for subsequent analyses

Last release features

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.



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GUSAR: Learn More

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.

GUSAR algorithm

The core of GUSAR consists of a unique algorithm of self-consistent regression that allows to select the best set of descriptors for a robust and reliable QSAR model.
Chemical structures are represented by MNA (Multilevel Neighborhood of Atoms) or QNA (Quantitative Neighbourhoods of Atoms) descriptors and biological activity descriptors that are based on the PASS prediction results for more than 8000 biological activities. QNA descriptors easily reflect the nature of intermolecular interactions. Models developed using biological activity descriptors enable to reveal key mechanisms of action of complex biological effects. MNA and QNA descriptors are used to calculate several variables, such as topological length and volume or lipophilicity of a molecule. For further details, see Filimonov et al. (2009), Zakharov et al. (2012), Zakharov et al. (2016).

GUSAR in comparison

In comparison with a number of 3D and 2D QSAR methods, the predictivity of GUSAR was superior to that of most other QSAR methods both on heterogeneous and on homogeneous data sets.
GUSAR comparison
Comparison of different QSAR approaches; shown is the performance of GUSAR relative to other methods.

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.

Precomputed GUSAR models

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).

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Information downloads

GUSAR Flyer (download)
GUSAR installation and activation procedure (download)
GUSAR Models (download; pdf, 0.54 MB)
GUSAR User Guide (download; pdf, 0.895 MB)
See also the GUSAR Homepage of the developers.
The GUSAR program package is under copyright protection (©) of Zakharov A.V., Filimonov D.A., Poroikov V.V., Lagunin A.A., Russian State Patent Agency Certificate, No. 2006613591 of 15.09.2006.