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
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
Uploading of SD files for batch predictionsNGS data analysis is supported by the platform. ChIP-seq data sets containing in vivo transcription factor binding sites or methylation results can be analyzed with the help of ready-made workflows. Galaxy tools are integrated, supporting RNA-seq data analysis, and many functions more.

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    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.

    Precomputed GUSAR 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, acute mouse toxicity or antitargets (off-targets). Click here to learn more.

    Information downloads

    GUSAR Flyer (download)
    GUSAR installation and activation procedure (download)
    GUSAR Models (download; pdf, 0.54 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.


    Dmitriev A, Rudik A, Filimonov D, Lagunin A, Pogodin P, Dubovskaja V, Bezhentsev V, Ivanov S, Druzhilovskiy DS, Tarasova O, Poroikov V. (2017) Integral estimation of xenobiotics’ toxicity with regard to their metabolism in human organism. Pure and Applied Chemistry 89(10), 1449–1458. Link

    Rudik AV, Bezhentsev VM, Dmitriev AV, Druzhilovskiy DS, Lagunin AA, Filimonov DA, Poroikov VV. MetaTox: Web Application for Predicting Structure and Toxicity of Xenobiotics’ Metabolites. (2017) J Chem Inf Model. 57, 638-642. Link

    Ozturk I.I., Yarar S., Banti C.N., Kourkoumelis N., Chrysouli M.P., Manoli M., Tasiopoulos A.J., Hadjikakou S.K. (2017). QSAR studies on antimony(III) halide complexes with N-substituted thiourea derivatives, Polyhedron, 123, 152-161. Link

    Zakharov A.V., Varlamova E.V., Lagunin A.A., Dmitriev A.V., Muratov E.N., Fourches D., Kuz’min V.E., Poroikov V.V., Tropsha A., Nicklaus M.C. (2016). QSAR Modeling and Prediction of Drug-Drug Interactions. Molecular Pharmaceutics, 13 (2), 545–556. Link

    Goncharuk V.V., Buben A.L., Borisenok O.A., Kozlovskii V.I., Pun’ko I. M., Vdovichenko V.P., Praliev K.D. (2016). Analgesic activity of some new decahydroquinoline derivatives. Experimental and Clinical Pharmacology, 79 (11), 7-10 (Rus). Link

    Brazhkо O.O. The biological activity of the derivatives of 2-methyl-(phenyl) substituted (quinoline-4-ylthio) carboxylic acids. Ph.D. Thesis (Bioorganic chemistry). Institute of Bioorganic Chemistry and Petrochemistry NAS of Ukraine, Kiev, Ukraine, 2016. 242 p.p. Link

    Mansouri K., Abdelaziz A., Rybacka A. et al. (2016). CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environmental Health Perspectives, 124 (7), 1023-1033. Full text.

    Hadjikakou S.K., Ozturka I.I., Banti C.N., Kourkoumelis N., Hadjiliadis N. (2015). Recent advances on antimony(III/V) compounds with potential activity against tumor cells. Journal of Inorganic Biochemistry, 153, 293–305. Link

    Khayrullina V.R., Gerchikov A.Ya., Lagunin A.A., Zarudii F.S. (2015). Quantitative analysis of structure-activity relationships of tetrahydro-2H-isoindole cyclooxygenase-2 inhibitors. Biochemistry Moscow, 80 (1), 74-86. Link

    Fedorova, E.V., Buryakina, A.V., Zakharov, A.V., Filimonov, D.A., Lagunin, A.A., Poroikov, V.V. (2014) Design, synthesis and pharmacological evaluation of novel vanadium-containing complexes as antidiabetic agents. PLoS One 9:e100386. doi: 10.1371/journal.pone.0100386. PMID: 25057899

    Lagunin A.A., Gloriozova T.A., Dmitriev A.V., Volgina N.E., Poroikov V.B. (2013). Computer Evaluation of Drug Interactions with P-Glycoprotein. Bull. Exp. Biol. Med., 154 (4), 521-524. Link

    Mahajan D.T., Masand V.H., Patil K.N., Hadda T.B., Rastija V. (2013). Integrating GUSAR and QSAR analyses for antimalarial activity of synthetic prodiginines against multi drug resistant strain. Med. Chem. Res., 22 (5), 2284–2292. Link

    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). Link

    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. PMID: 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. PMID: 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. PMID: 21802177.

    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. PMID: 20024804

    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. PMID: 11128093

    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.