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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.

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: In recent years as one of the promising approaches in QSAR modeling Monte Carlo optimization approach as conformation independent method has emerged. Monte Carlo optimization has proven to be valuable tool in chemoinformatics and this review presents its application in drug discovery and design. In this review the basic principles and important features of these methods are discussed as well as the advantages of conformation independent optimal descriptors developed from molecular graph and Simplified Molecular Input Line Entry System (SMILES) notation compared to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results from Monte Carlo optimization based QSAR modeling with the further addition of molecular docking studies applied for various pharmacologically important endpoints. SMILES notation based optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/decrease of considered biological activity and which are used further to design compounds with targeted activity based on computer calculation are presented. In this mini review research papers in which molecular docking was applied as additional method to designed molecules to validate their activity further are summarized. These papers present very good correlation among results obtained from Monte Carlo optimization modeling and molecular docking studies.