MARCH-INSIDE: Markov Chain Invariants for Networks Simulation and Design MARCH-INSIDE (MI), is a well-known method introduced by Prof. Humbert G Díaz (Gonzaléz-Díaz et al.) as early as 2002 for the calculation of Markov Invariants (Moments, Shanon entropies, Mean Markov values) of molecular graphs and complext netxorks using a Markov chain stchastic approach. In case you want to develop new collaborations, applications, etc. related to MI algorith please do not hesitate to contact us at: https://www.linkedin.com/in/humbertgdiaz/
Main authors contributions:
H. Gónzalez-Díaz (algorithm and software design, AI/ML applications, main author of papers, https://github.com/glezdiazh),
I. Sánchez-Hernandez (software programming for version 1.0 and higher and co-author of papers, not github profile available),
R. Molina (software programming for version 2.0 and higher and co-author of papers, https://github.com/rmrsint).
Applications: MI parameters can be used to study the Markov Chain stochastic behaviour of graph or network-like systems, quantify the structure of complex biomo-lecular systems, and/or as input of Artificial Intelligence / Machine Learning (AI/ML) algorithms in order to seek predictive models. MI parameters have been to predict properties of small-sized drugs, proteins sequences, proteins 3D structures, RNA secondary structures, metabolic networks, criminal causality networks, biological networks, social networks, etc.
Posterior Algorithms:
MI algorithm is the basis for posterior softwares developmed a posteriori by other authors in co-authorship with Prof González-Díaz or independently. Some of these are: Sequence to Stars Networks (S2SNET) by C.R. Munteanu and González-Díaz H. https://github.com/muntisa/S2SNet); R-Markov Topological Indices (RMARKOVTI) by C.R. Munteanu https://github.com/muntisa/RMarkovTI, S2SNET Phyton (PyS2SNET) by C.R. Munteanu https://github.com/muntisa/pyS2SNet, etc.
See references:
01: González Díaz H, Olazabal E, Castañedo N, Sánchez IH, Morales A, Serrano HS, González J, de Armas RR. Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer aided molecular design II: experimental and theoretical assessment of a novel method for virtual screening of fasciolicides. J Mol Model. 2002 Aug;8(8):237-45. doi: 10.1007/s00894-002-0088-7. PMID: 12324800.
02: Díaz HG, Sánchez IH, Uriarte E, Santana L. Symmetry considerations in Markovian chemicals 'in silico' design (MARCH-INSIDE) I: central chirality codification, classification of ACE inhibitors and prediction of sigma-receptor antagonist activities. Comput Biol Chem. 2003 Jul;27(3):217-27. doi: 10.1016/s0097-8485(02)00053-0. PMID: 12927098.
03: Gonzáles-Díaz H, Gia O, Uriarte E, Hernádez I, Ramos R, Chaviano M, Seijo S, Castillo JA, Morales L, Santana L, Akpaloo D, Molina E, Cruz M, Torres LA, Cabrera MA. Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds. J Mol Model. 2003 Dec;9(6):395-407. doi: 10.1007/s00894-003-0148-7. Epub 2003 Sep 16. PMID: 13680309.
04: Ramos de Armas R, González Díaz H, Molina R, Pérez González M, Uriarte E. Stochastic-based descriptors studying peptides biological properties: modeling the bitter tasting threshold of dipeptides. Bioorg Med Chem. 2004 Sep 15;12(18):4815-22. doi: 10.1016/j.bmc.2004.07.017. PMID: 15336260.
05: González-Díaz H, Agüero G, Cabrera MA, Molina R, Santana L, Uriarte E, Delogu G, Castañedo N. Unified Markov thermodynamics based on stochastic forms to classify drugs considering molecular structure, partition system, and biological species: distribution of the antimicrobial G1 on rat tissues. Bioorg Med Chem Lett. 2005 Feb 1;15(3):551-7. doi: 10.1016/j.bmcl.2004.11.059. PMID: 15664811.
06: González-Díaz H, Torres-Gómez LA, Guevara Y, Almeida MS, Molina R, Castañedo N, Santana L, Uriarte E. Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design III: 2.5D indices for the discovery of antibacterials. J Mol Model. 2005 Mar;11(2):116-23. doi: 10.1007/s00894-004-0228-3. Epub 2005 Feb 19. PMID: 15723208.
07: González-Díaz H, Uriarte E. Biopolymer stochastic moments. I. Modeling human rhinovirus cellular recognition with protein surface electrostatic moments. Biopolymers. 2005 Apr 5;77(5):296-303. doi: 10.1002/bip.20234. PMID: 15648087.
08: de Armas RR, Díaz HG, Molina R, Uriarte E. Stochastic-based descriptors studying biopolymers biological properties: extended MARCH-INSIDE methodology describing antibacterial activity of lactoferricin derivatives. Biopolymers. 2005 Apr 5;77(5):247-56. doi: 10.1002/bip.20202. PMID: 15682438.
09: Santana L, Uriarte E, González-Díaz H, Zagotto G, Soto-Otero R, Méndez- Alvarez E. A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins. J Med Chem. 2006 Feb 9;49(3):1149-56. doi: 10.1021/jm0509849. PMID: 16451079.
10: Cruz-Monteagudo M, González-Díaz H, Borges F, González-Díaz Y. Simple stochastic fingerprints towards mathematical modeling in biology and medicine. 3. Ocular irritability classification model. Bull Math Biol. 2006 Oct;68(7):1555-72. doi: 10.1007/s11538-006-9083-y. Epub 2006 Jul 25. PMID: 16865609.
11: González-Díaz H, Olazábal E, Santana L, Uriarte E, González-Díaz Y, Castañedo N. QSAR study of anticoccidial activity for diverse chemical compounds: prediction and experimental assay of trans-2-(2-nitrovinyl)furan. Bioorg Med Chem. 2007 Jan 15;15(2):962-8. doi: 10.1016/j.bmc.2006.10.032. Epub 2006 Oct 19. PMID: 17081758.
12: González-Díaz H, Bonet I, Terán C, De Clercq E, Bello R, García MM, Santana L, Uriarte E. ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. Eur J Med Chem. 2007 May;42(5):580-5. doi: 10.1016/j.ejmech.2006.11.016. Epub 2006 Dec 15. PMID: 17207560.
13: González-Díaz H, Prado-Prado F, Ubeira FM. Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. Curr Top Med Chem. 2008;8(18):1676-90. doi: 10.2174/156802608786786543. PMID: 19075774.
14: Cruz-Monteagudo M, González-Díaz H, Borges F, Dominguez ER, Cordeiro MN. 3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. quantitative proteome-toxicity relationships (QPTR) based on mass spectrum spiral entropy. Chem Res Toxicol. 2008 Mar;21(3):619-32. doi: 10.1021/tx700296t. Epub 2008 Feb 8. PMID: 18257557.
15: Cruz-Monteagudo M, Munteanu CR, Borges F, Cordeiro MN, Uriarte E, González- Díaz H. Quantitative Proteome-Property Relationships (QPPRs). Part 1: finding biomarkers of organic drugs with mean Markov connectivity indices of spiral networks of blood mass spectra. Bioorg Med Chem. 2008 Nov 15;16(22):9684-93. doi: 10.1016/j.bmc.2008.10.004. Epub 2008 Oct 5. PMID: 18951807.
16: Concu R, Dea-Ayuela MA, Perez-Montoto LG, Prado-Prado FJ, Uriarte E, Bolás- Fernández F, Podda G, Pazos A, Munteanu CR, Ubeira FM, González-Díaz H. 3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites. Biochim Biophys Acta. 2009 Dec;1794(12):1784-94. doi: 10.1016/j.bbapap.2009.08.020. Epub 2009 Aug 28. PMID: 19716935.
17: González-Díaz H, Romaris F, Duardo-Sanchez A, Pérez-Montoto LG, Prado-Prado F, Patlewicz G, Ubeira FM. Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. Curr Pharm Des. 2010;16(24):2737-64. doi: 10.2174/138161210792389234. PMID: 20642428.
18: González-Díaz H, Duardo-Sanchez A, Ubeira FM, Prado-Prado F, Pérez-Montoto LG, Concu R, Podda G, Shen B. Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. Curr Drug Metab. 2010 May;11(4):379-406. doi: 10.2174/138920010791514225. PMID: 20446904.
19: Agüero-Chapin G, Pérez-Machado G, Molina-Ruiz R, Pérez-Castillo Y, Morales- Helguera A, Vasconcelos V, Antunes A. TI2BioP: Topological Indices to BioPolymers. Its practical use to unravel cryptic bacteriocin-like domains. Amino Acids. 2011 Feb;40(2):431-42. doi: 10.1007/s00726-010-0653-9. Epub 2010 Jun 19. PMID: 20563611.
20: Agüero-Chapin G, de la Riva GA, Molina-Ruiz R, Sánchez-Rodríguez A, Pérez- Machado G, Vasconcelos V, Antunes A. Non-linear models based on simple topological indices to identify RNase III protein members. J Theor Biol. 2011 Mar 21;273(1):167-78. doi: 10.1016/j.jtbi.2010.12.019. Epub 2010 Dec 28. PMID: 21192951.
21: González-Díaz H, Prado-Prado F, García-Mera X, Alonso N, Abeijón P, Caamaño O, Yáñez M, Munteanu CR, Pazos A, Dea-Ayuela MA, Gómez-Muñoz MT, Garijo MM, Sansano J, Ubeira FM. MIND-BEST: Web server for drugs and target discovery; design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from Trichomonas gallinae. J Proteome Res. 2011 Apr 1;10(4):1698-718. doi: 10.1021/pr101009e. Epub 2011 Feb 24. PMID: 21184613.
22: Prado-Prado F, García-Mera X, Abeijón P, Alonso N, Caamaño O, Yáñez M, Gárate T, Mezo M, González-Warleta M, Muiño L, Ubeira FM, González-Díaz H. Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. Eur J Med Chem. 2011 Apr;46(4):1074-94. doi: 10.1016/j.ejmech.2011.01.023. Epub 2011 Jan 21. PMID: 21315497.
23: García I, Fall Y, Gómez G, González-Díaz H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti- bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol Divers. 2011 May;15(2):561-7. doi: 10.1007/s11030-010-9280-3. Epub 2010 Oct 8. PMID: 20931280.
24: González-Díaz H, Prado-Prado F, Sobarzo-Sánchez E, Haddad M, Maurel Chevalley S, Valentin A, Quetin-Leclercq J, Dea-Ayuela MA, Teresa Gomez-Muños M, Munteanu CR, José Torres-Labandeira J, García-Mera X, Tapia RA, Ubeira FM. NL MIND-BEST: a web server for ligands and proteins discovery--theoretic- experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. J Theor Biol. 2011 May 7;276(1):229-49. doi: 10.1016/j.jtbi.2011.01.010. Epub 2011 Jan 26. PMID: 21277861.
25: González-Díaz H, Muíño L, Anadón AM, Romaris F, Prado-Prado FJ, Munteanu CR, Dorado J, Sierra AP, Mezo M, González-Warleta M, Gárate T, Ubeira FM. MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens. Mol Biosyst. 2011 Jun;7(6):1938-55. doi: 10.1039/c1mb05069a. Epub 2011 Apr 6. PMID: 21468430.
26: Prado-Prado F, García-Mera X, Escobar M, Sobarzo-Sánchez E, Yañez M, Riera- Fernandez P, González-Díaz H. 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins. Eur J Med Chem. 2011 Dec;46(12):5838-51. doi: 10.1016/j.ejmech.2011.09.045. Epub 2011 Oct 1. PMID: 22005185.
27: Prado-Prado F, García-Mera X, Escobar M, Alonso N, Caamaño O, Yañez M, González-Díaz H. 3D MI-DRAGON: new model for the reconstruction of US FDA drug- target network and theoretical-experimental studies of inhibitors of rasagiline derivatives for AChE. Curr Top Med Chem. 2012;12(16):1843-65. PMID: 23030618.
28: González-Díaz H, Munteanu CR, Postelnicu L, Prado-Prado F, Gestal M, Pazos A. LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria. Mol Biosyst. 2012 Mar;8(3):851-62. doi: 10.1039/c2mb05432a. Epub 2012 Jan 10. PMID: 22234525.
29: González-Díaz H, Arrasate S, Sotomayor N, Lete E, Munteanu CR, Pazos A, Besada-Porto L, Ruso JM. MIANN models in medicinal, physical and organic chemistry. Curr Top Med Chem. 2013;13(5):619-41. doi: 10.2174/1568026611313050006. PMID: 23548024.
30: Alonso N, Caamaño O, Romero-Duran FJ, Luan F, D S Cordeiro MN, Yañez M, González-Díaz H, García-Mera X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem Neurosci. 2013 Oct 16;4(10):1393-403. doi: 10.1021/cn400111n. Epub 2013 Jul 29. PMID: 23855599; PMCID: PMC3799003.
31: Munteanu CR, Pedreira N, Dorado J, Pazos A, Pérez-Montoto LG, Ubeira FM, González-Díaz H. LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design. Mol Inform. 2014 Apr;33(4):276-85. doi: 10.1002/minf.201300027. Epub 2014 Mar 18. PMID: 27485774.
32: Aguirre-Crespo F, García-Mera X, Guillén-Poot MA, May-Díaz HF, Tun-Suárez A, Aguirre-Crespo A, Hernández-Rodríguez J, Vergara-Galicia J, Rodríguez-López V, Prado-Prado FJ. Review of Theoretical Prediction Models for Organic Extract Metabolites, Effect of Drying Temperature on Smooth Muscle Relaxing Activity Induced by Organic Extracts Specially Cecropia Obtusifolia Portal and Web Server Predictors of Drug-Protein Interaction. Mini Rev Med Chem. 2015 Feb 19. Epub ahead of print. PMID: 25694070.
33: Agüero-Chapin G, Galpert D, Molina-Ruiz R, Ancede-Gallardo E, Pérez-Machado G, de la Riva GA, Antunes A. Graph Theory-Based Sequence Descriptors as Remote Homology Predictors. Biomolecules. 2019 Dec 23;10(1):26. doi: 10.3390/biom10010026. PMID: 31878100; PMCID: PMC7022958.