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).