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
Current Drug Metabolism
Title: Review of MARCH-INSIDE & Complex Networks Prediction of Drugs: ADMET, Anti-parasite Activity, Metabolizing Enzymes and Cardiotoxicity Proteome Biomarkers
Volume: 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
Affiliation:
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
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.
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Gonzalez-Diaz Humberto, Duardo-Sanchez Aliuska, M. Ubeira Florencio, Prado-Prado Francisco, G. Perez-Montoto Lazaro, Concu R., Podda Gianni and Shen Bairong, Review of MARCH-INSIDE & Complex Networks Prediction of Drugs: ADMET, Anti-parasite Activity, Metabolizing Enzymes and Cardiotoxicity Proteome Biomarkers, Current Drug Metabolism 2010; 11 (4) . https://dx.doi.org/10.2174/138920010791514225
DOI https://dx.doi.org/10.2174/138920010791514225 |
Print ISSN 1389-2002 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5453 |
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