Review of MARCH-INSIDE & Complex Networks Prediction of Drugs: ADMET, Anti-parasite Activity, Metabolizing Enzymes and Cardiotoxicity Proteome Biomarkers

Humberto      Gonzalez-Diaz; Aliuska      Duardo-Sanchez; Florencio      M. Ubeira; Francisco      Prado-Prado; Lazaro      G. Perez-Montoto; R.      Concu; Gianni      Podda; Bairong      Shen

doi:10.2174/138920010791514225

Abstract

In this communication we carry out an in-depth review of a very versatile QSPR-like method. The method name is MARCH-INSIDE (MARkov CHains Ivariants for Network Selection and DEsign) and is a simple but efficient computational approach to the study of QSPR-like problems in biomedical sciences. The method uses the theory of Markov Chains to generate parameters that numerically describe the structure of a system. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining structures of molecular, macromolecular, supramolecular, and non-molecular systems within large databases. Here, we review and comment by the first time on the several applications of MARCH-INSIDE to predict drugs ADMET, Activity, Metabolizing Enzymes, and Toxico-Proteomics biomarkers discovery. The MARCH-INSIDE models reviewed are: a) drug-tissue distribution profiles, b) assembling drug-tissue complex networks, c) multi-target models for anti-parasite/anti-microbial activity, c) assembling drug-target networks, d) drug toxicity and side effects, e) web-server for drug metabolizing enzymes, f) models in drugs toxico-proteomics. We close the review with some legal remarks related to the use of this class of QSPR-like models.

Keywords: QSPR/QSAR models, ADMET, Drug-Tissue distribution networks, Anti-parasite agents networks, Drugs Toxico-Proteomics, Drug Metabolizing Enzymes, Topological Indices, Markov Chains, Graph theory, Complex networks