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

[ Vol. 11 , Issue. 4 ]

Author(s):

Humberto Gonzalez-Diaz, Aliuska Duardo-Sanchez, Florencio M. Ubeira, Francisco Prado-Prado, Lazaro G. Perez-Montoto, R. Concu, Gianni Podda and Bairong Shen   Pages 379 - 406 ( 28 )

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

Affiliation:

Faculty of Pharmacy, University of Santiago de Compostela, 15782, Spain.



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