SMILES notation based optimal descriptors as a universal tool for the QSAR analysis with
further application in drug discovery and design is presented. The basis of this QSAR modeling is
Monte Carlo method which has important advantages over other methods, like the possibility of
analysis of a QSAR as a random event, is discussed. The advantages of SMILES notation based optimal
descriptors in comparison to commonly used descriptors are defined. The published results of
QSAR modeling with SMILES notation based optimal descriptors applied for various pharmacologically
important endpoints are listed. The presented QSAR modeling approach obeys OECD principles
and has mechanistic interpretation with possibility to identify molecular fragments that contribute in
positive and negative way to studied biological activity, what is of big importance in computer aided drug design of new
compounds with desired activity.
Keywords: CORAL software, Drug design, Monte Carlo method, Optimal descriptor, QSAR, SMILES.
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