GUSAR

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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.

The 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 4000 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).

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 evaluation
Comparison of different QSAR approaches; shown is the performance of GUSAR relative to other methods. (Click for an enlarged view.)

Further information:
GUSAR Flyer (download; pdf, 0.35 MB)
GUSAR Presentation (download; pdf, 0.93 MB)
GUSAR Models (download; pdf, 0.54 MB)
See also the GUSAR Homepage of the developers.
Note:
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.

Recent independent GUSAR application:
Vijay H. Masand et al., General Unrestricted Structure Activity Relationships based evaluation of quinoxaline derivatives as potential influenza NS1A protein inhibitors, Der Pharma Chemica 3(4):517-525 (2011).

Price information (GUSAR program)
Academic one seat 1-year license 1000 € order
Commercial one seat 1-year license    2000 €  order
Free trial version    0 € try it out
All prices are without VAT. Appropriate taxes will be added, depending on your location.

Price information (GUSAR models)
Each model
Academic one seat 1-year license 1000 €   order
Commercial one seat 1-year license    2000 €  order
All prices are without VAT. Appropriate taxes will be added, depending on your location.
Get a picture of GUSAR
GUSAR screenshot
Quantitative prediction of the effect of a chemical compound. (Click for an enlarged view.)

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



Among its key features are:
Unique descriptors and mathematical algorithms
High speed of predictions
Easy-to-use interface
Selection of the most predictive models
Estimation which parts of a molecule provide positive and negative impact to the activity
Uploading of SD files for batch predictions
Saving GUSAR output predictions in SDF and CSV formats for subsequent analyses

New features of release 2011:
• 
Enhanced choice of variables:
• 
Topological length of a molecule
• 
Topological volume of a molecule
• 
Calculated lipophilicity
Improved algorithm of model building and prediction
Automatic, simplified creation of QSAR models

Precomputed GUSAR models
Additionally to the GUSAR program, we provide ready-trained GUSAR models to predict certain biological activities. These are SAR bases that can be used with the GUSAR software for predictions on acute rat toxicity or antitargets (off-targets). Click here to learn more (pdf, 0.6 MB).
 

Recent GUSAR publications:

Lagunin, A.A., Gloriozova, T.A., Dmitriev, A.V., Volgina, N.E., Poroikov, V.V. (2013) Computer evaluation of drug interactions with P-glycoprotein. Bull. Exp. Biol. Med. 154:521–524. 23486596.

Zakharov, A.V., Lagunin, A.A., Filimonov, D.A., Poroikov, V.V. (2012) Quantitative prediction of antitarget interaction profiles for chemical compounds. Chem. Res. Toxicol. 25:2378–2385. 23078046.

Zakharov, A.V., Peach, M.L., Sitzmann, M., Filippov, I.V., McCartney, H.J., Smith, L.H., Pugliese, A., Nicklaus, M.C. (2012) Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med. Chem. 4:1933–1944. 23088274.

Kokurkina G.V., Dutov M.D., Shevelev S.A., Popkov S.V., Zakharov A.V., Poroikov V.V. (2011) Synthesis, antifungal activity and QSAR study of 2-arylhydroxynitroindoles. Eur. J. Med. Chem. 46:4374–4382. PubMed.

Lagunin A., Zakharov A., Filimonov D., Poroikov V. (2011) QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol. Inform. 30:241–250. Link.

Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009) QNA based 'Star Track' QSAR approach. SAR QSAR Environ. Res. 20:679-709. PubMed

Lagunin A., Zakharov A., Filimonov D., Poroikov V. (2009). In silico assessment of acute toxicity in rodents. Toxicol. Lett. 189:S264.

Filimonov D.A., Poroikov V.V. (2008) Probabilistic approach in activity prediction. In: Chemoinformatics Approaches to Virtual Screening. Eds. Alexandre Varnek and Alexander Tropsha. Cambridge (UK): RSC Publishing, pp. 182-216.

Filimonov D.A., Poroikov V.V. (2006) Prediction of biological activity spectrum for organic compounds. Russ. Chem. J. 50: 66-75.

Filimonov D.A., Lagunin A.A., Poroikov V.V. (2005) Prediction of activity spectra for substances using new local integrative descriptors. QSAR and Molecular Modelling in Rational Design of Bioactive Molecules. Eds. Esin Aki Sener, Ismail Yalcin, Ankara (Turkey), CADD & D Society, pp. 98-99.

Poroikov V. V., Filimonov D. A., Borodina Yu. V., Lagunin A. A., Kos A. (2000) Robustness of Biological Activity Spectra Predicting by Computer Program PASS for Noncongeneric Sets of Chemical Compounds. J. Chem. Inf. Comput. Sci. 40:1349-1355. PubMed

Filimonov D., Poroikov V., Borodina Yu., Gloriozova T. (1999) Chemical Similarity Assessment through Multilevel Neighborhoods of Atoms: Definition and Comparison with the Other Descriptors. J. Chem. Inf. Comput. Sci. 39:666-670.Link

Poroikov V. V., Filimonov D. A., Borodina Yu. V., Gloriozova T. A., Sitnikov V. B., Sadovnikov S. V., Sosnov A. V. (2004) Quantitative relationships between structure and delayed neurotoxicity of chemicals studied by the Self-Consistent Regression method using the PASS program. Pharmaceutical Chemistry Journal 38:188-190.

Filimonov D. A., Akimov D. V., Poroikov V. V. (2004) The method of Self-Consistent Regression for the quantitative analysis of relationships between structure and properties of chemicals. Pharmaceutical Chemistry Journal 38:21-24.