Predicting Antimicrobial Drugs and Targets with the MARCH-INSIDE Approach

The method MARCH-INSIDE (MARkovian CHemicals IN SIlico DEsign) is a simple but efficient computational approach to the study of Quantitative Structure-Activity Relationships (QSAR) in Medicinal Chemistry. The method uses the theory of Markov Chains to generate parameters that numerically describe the chemical structure of drugs and drug targets. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs) and stochastic 3D-Topographic Indices (sto-TPGIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining of molecular and macromolecular chemical structures within large databases. In the work described here, we review and comment on the several applications of MARCH-INSIDE to the Medicinal Chemistry of Antimicrobial agents as well as their molecular targets. First we revised the use of classic sto-TIs to predict antiparasite compounds for the treatment of Fascioliasis. Next, we revised the use of chiral sto-TIs (sto-CTIs) to predict new antibacterial, antiviral and anti-coccidial compounds. After that, we review multi-target sto-TIs (mt-sto-TIs), which unifying QSAR models predicting antifungal, antibacterial, or anti-parasite drugs with multiple targets (microbial species). We also discussed the uses of mt-sto-TIs to assemble drug-drug similarity Complex Networks of antimicrobial compounds based on molecular structure. Last, we review the use of MARCH-INSIDE to generate macromolecular TIs and TPGIs for proteins or RNA targets for antimicrobial drugs.