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Sobre nosotros
Sobre nosotros
IKERDATA, S.L. es una empresa “start-up” fundada por profesores de la UPV/EHU, de la Fundación IKERBASQUE y de la Universidad de A Coruña (UDC), con el apoyo de ZITEK, el programa de apoyo al emprendimiento del Campus de Bizkaia, para poner en valor sus resultados de Investigación y desarrollo en acciones de transferencia que ayuden a mejorar la calidad asistencial de los servicios públicos y la competitividad del sector productivo de nuestro entorno o área de influencia. Estamos enfocados a Servicios de Análisis, Consultoría, Formación, Asociación de Consorcios de Investigación, Difusión del Conocimiento, etc.
Nos especializamos en la aplicación de técnicas y procedimientos de Computación avanzada, incluyendo la Inteligencia Artificial (IA), estadística, redes complejas, bioinformática, quimioinformática, farmacoinformática,… para la toma de decisiones y optimización de recursos en el campo de la salud, de la Industria Farmacéutica (descubrimiento temprano y reposicionamiento de fármacos o diseño de vacuna); en la validación y descubrimiento de Biomarcadores, la Nanobiotecnología, la Biotecnología, la industria de combustibles y la ingeniería biomédica.
En IKERDATA, S.L. desarrollamos, validamos y comercializamos modelos predictivos cuyas funciones han sido diseñadas específicamente para cubrir la demanda de cada empresa o proyecto y diseñamos las herramientas necesarias para facilitar su uso. En todo caso asegurando la adecuada transferencia de tecnología al receptor de nuestros productos.
Los miembros de IKERDATA, S.L. tienen una amplia experiencia en la aplicación de técnicas y procedimientos de computación avanzada e inteligencia artificial en ámbitos como la quimioinformática, eHealth o la farmacoinformática.
Conforme las tecnologías asociadas a estos campos de actividad avanzan y se hacen más comunes, se incrementa la cantidad de publicaciones que reportan mayor eficiencia en los procesos implicados que al tratarse de novedosas combinaciones de técnicas muy especializadas, las empresas están interesadas en introducir, dentro de sus equipos de I+D+I, a expertos en estas tecnologías. El problema con el que se encuentran es que, actualmente, no hay personal suficientemente cualificado para la realización de estas tareas y es por ello que nuestra empresa pondrá énfasis en formar a profesionales en la utilización de estas técnicas.
También nos distinguimos por estar especializados en generar modelos predictivos para ensayos in silico específicos. Partiendo de un análisis del problema, se crea una base de datos o un almacén de datos “Datawarehouse” y se generan modelos capaces de predecir actividades y relaciones entre variables, frente a diferentes dianas y procesos. De esta forma, se ayuda a acortar, abaratar y ahorrar muchos recursos en las fases posteriores de los procesos. IKERDATA, S.L. apuesta por la especialización en el diseño computacional.
REFERENCIAS
Shaikh, Zaffar & Panhwar, Ali & Kumar, Kamlesh & Solangi, Irfan & Panhwar, Abida. (2019). Pharmacoinformatics: Development through History and Its Role in Pharmaceutical Industry. doi: 10.9734/jpri/2019/v31i530312
Frank K.Brown. (1998). Chemoinformatics: What is it and How does it Impact Drug Discovery. doi:10.1016/S0065-7743(08)61100-8
zitek.eus
Ph.D. M.D. Prof. Alejandro Pazos
Ph.D. Prof. Humberto G. Díaz
Ph.D. Prof. Cristian Robert Munteanu
Ph.D. Prof. Marcos Gestal
Servicios
Servicios
El equipo de IKERDATA, S.L. tiene experiencia en los siguientes ámbitos:
Cribado computacional de sistemas químicos
Predicción de nuevos fármacos, vacunas, biomarcadores, materiales, nanosistemas, mezcla de combustibles, etc.
Desarrollo de software y modelos a medida del cliente
Desarrollo y transferencia de software de fácil uso
Diseño de Software de Ingeniería Biomédica
Diseño de nuevas apps para diagnóstico médico, dispositivos médicos y procesamiento de Imágenes
Edición, publicación y comercialización de literatura quimioinformática
Edición, publicación y comercialización de literatura quimioinformática complementaria
Organización de conferencias y cursos de formación en I+D
Organización de Bootcamps, Prácticas de Verano, Becas Marie Curie (Institución anfitriona)
Computación avanzada en el ámbito de la salud, con dirección y participación en más de 50 proyectos y contratos en los últimos 20 años.
Los servicios que ofrecemos:
Cribado Computacional de Sistemas Químicos
Predicción de nuevos medicamentos, vacunas, biomarcadores, materiales, nanosistemas, mezclas de fuel, etc.
Desarrollo de Sofwares y Modelos diseñados para cada cliente
Desarrolllo y transferencia a Software User-Friendly
Diseño de softwares para ingeniería biomédica
Diseño de nuevas aplicaciones para diagnóstico médico, dispositivo médico, procesado de imágenes, etc.
Edición, publicación y comercialización de literatura quimioinformática
Edición, publicación y comercialización de literatura complemetaria en quimioinformática
Conferencias R&D y Organización de Cursos de Entrenamiento
Organización de Bootcamp, estancias de verano, Marie Curie Scholarships Host Institution
Modalidades
Modalidades
Industry Research Contracts
Contact us to develop research projects or services for your company!
Industry, University, Technological Centers Projects Partner
Invite us to be your partner in research projects funded by Industry or Agencies!
Members of our staff have been partners or managers of European Commission, MINECO, REPSOL-PETRONOR S.A., TECNALIA projects.
Scientific Consulting
Hire our staff experts for Consulting Services on Computational Drugs Discovery, Vaccine Design, Biomarkers Validation, Materials Design, Fuel Blends Optimization!
We have been Consultants for European Commission Innovative Medicines Initiative (IMI), ERC Consolidator Grants, USA National Science Foundation, BARD USA-Israel, BBSRC United Kingdom, BMBF Germany, ANR France, etc.
Trabajos y Estancias
Trabajos y Estancias
IKERDATA S.L. estamos interesados en candidatos para:
MSCA Marie Curie Global or International Individual Fellowships
ZITEK IKERDATA S.L. can act as your host or secondment institution offering the possibility of research stays at UPVEHU University Dept. of Organic and Inorganic Chemistry, Bilbao, Spain.
BIKAINTEK Basque Government Industrial PhD and Post-Doctoral Programs
We can be your Host to develop a PhD Industrial Thesis or Post-Doctoral Project.
UPVEHU & UDC PhD / MSc Thesis Dissertation Internships
Do Industrial Internships with us to work in Research Projects Leading to MSc Thesis dissertation in different UPVEHU or UDC MSc Programs!
Publicaciones
Publicaciones
PUBLICACIONES
1. Nocedo-Mena, D.; Arrasate, S.; Garza-Gonzalez, E.; Rivas-Galindo, V. M.; Romo-Mancillas, A.; Munteanu, C. R.; Sotomayor, N.; Lete, E.; Barbolla, I.; Martin, C. A.; Del Rayo Camacho-Corona, M., Molecular docking, SAR analysis and biophysical approaches in the study of the antibacterial activity of ceramides isolated from Cissus incisa. Bioorg. Chem. 2021, 109, 104745.
2. Montes-Bageneta, I.; Akesolo, U.; Lopez, S.; Merino, M.; Anakabe, E.; Arrasate, S., Pollutants in Organic Chemistry and Medicinal Chemistry Education Laboratory. Experimental and Machine Learning Studies. Curr Top Med Chem 2020, 20, 720-730.
3. Arrasate, S.; Duardo-Sanchez, A., Perturbation Theory Machine Learning Models: Theory, Regulatory Issues, and Applications to Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. Curr Top Med Chem 2018, 18, 1203-1213.
4. Lete, E.; Sotomayor, N.; Arrasate, S., Enantioselective synthesis in organic and medicinal chemistry. Curr Top Med Chem 2014, 14, 1209-11.
5. Vallejo, A.; Olivares, M.; Fernandez, L. A.; Etxebarria, N.; Arrasate, S.; Anakabe, E.; Usobiaga, A.; Zuloaga, O., Optimization of comprehensive two dimensional gas chromatography-flame ionization detection-quadrupole mass spectrometry for the separation of octyl- and nonylphenol isomers. J. Chromatogr. A 2011, 1218, 3064-9.
6. Navarro, P.; Bustamante, J.; Vallejo, A.; Prieto, A.; Usobiaga, A.; Arrasate, S.; Anakabe, E.; Puy-Azurmendi, E.; Zuloaga, O., Determination of alkylphenols and 17beta-estradiol in fish homogenate. Extraction and clean-up strategies. J. Chromatogr. A 2010, 1217, 5890-5.
7. Cortazar, E.; Bartolome, L.; Arrasate, S.; Usobiaga, A.; Raposo, J. C.; Zuloaga, O.; Etxebarria, N., Distribution and bioaccumulation of PAHs in the UNESCO protected natural reserve of Urdaibai, Bay of Biscay. Chemosphere 2008, 72, 1467-1474.
8. Garcia, E.; Arrasate, S.; Lete, E.; Sotomayor, N., Diastereoselective intramolecular alpha-amidoalkylation reactions of L-DOPA derivatives. Asymmetric synthesis of pyrrolo[2,1-a]isoquinolines. The Journal of organic chemistry 2005, 70, 10368-74.
9. Alvarellos, A.; Gestal, M.; Dorado, J.; Rabunal, J. R., Developing a Secure Low-Cost Radon Monitoring System. Sensors 2020, 20.
10. Herhaus, L.; Bhaskara, R. M.; Lystad, A. H.; Gestal-Mato, U.; Covarrubias-Pinto, A.; Bonn, F.; Simonsen, A.; Hummer, G.; Dikic, I., TBK1-mediated phosphorylation of LC3C and GABARAP-L2 controls autophagosome shedding by ATG4 protease. EMBO Rep 2020, 21, e48317.
11. Fernandez-Lozano, C.; Seoane, J. A.; Gestal, M.; Gaunt, T. R.; Dorado, J.; Pazos, A.; Campbell, C., Texture analysis in gel electrophoresis images using an integrative kernel-based approach. Scientific reports 2016, 6, 19256.
12. Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabunal, J. R.; Dorado, J.; Pazos, A., Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification. ScientificWorldJournal 2013, 2013, 982438.
13. Aguiar-Pulido, V.; Seoane, J. A.; Gestal, M.; Dorado, J., Exploring patterns of epigenetic information with data mining techniques. Curr. Pharm. Des. 2013, 19, 779-89.
14. Gomez-Carracedo, M. P.; Gestal, M.; Dorado, J.; Andrade, J. M., Chemically driven variable selection by focused multimodal genetic algorithms in mid-IR spectra. Anal Bioanal Chem 2007, 389, 2331-42.
15. Lopez-Cortes, A.; Guevara-Ramirez, P.; Kyriakidis, N. C.; Barba-Ostria, C.; Leon Caceres, A.; Guerrero, S.; Ortiz-Prado, E.; Munteanu, C. R.; Tejera, E.; Cevallos-Robalino, D.; Gomez-Jaramillo, A. M.; Simbana-Rivera, K.; Granizo-Martinez, A.; Perez, M. G.; Moreno, S.; Garcia-Cardenas, J. M.; Zambrano, A. K.; Perez-Castillo, Y.; Cabrera-Andrade, A.; Puig San Andres, L.; Proano-Castro, C.; Bautista, J.; Quevedo, A.; Varela, N.; Quinones, L. A.; Paz, Y. M. C., In silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19. Frontiers in pharmacology 2021, 12, 598925.
16. Tejera, E.; Munteanu, C. R.; Lopez-Cortes, A.; Cabrera-Andrade, A.; Perez-Castillo, Y., Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 M(pro) Protease. Molecules (Basel, Switzerland) 2020, 25.
17. Alvarez-Coiradas, E.; Munteanu, C. R.; Diaz-Saez, L.; Pazos, A.; Huber, K. V. M.; Loza, M. I.; Dominguez, E., Discovery of novel immunopharmacological ligands targeting the IL-17 inflammatory pathway. Int Immunopharmacol 2020, 89, 107026.
18. Linares-Blanco, J.; Munteanu, C. R.; Pazos, A.; Fernandez-Lozano, C., Molecular docking and machine learning analysis of Abemaciclib in colon cancer. BMC molecular and cell biology 2020, 21, 52.
19. Lopez-Cortes, A.; Cabrera-Andrade, A.; Vazquez-Naya, J. M.; Pazos, A.; Gonzales-Diaz, H.; Paz, Y. M. C.; Guerrero, S.; Perez-Castillo, Y.; Tejera, E.; Munteanu, C. R., Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks. Scientific reports 2020, 10, 8515.
20. Puente-Castro, A.; Fernandez-Blanco, E.; Pazos, A.; Munteanu, C. R., Automatic assessment of Alzheimer's disease diagnosis based on deep learning techniques. Comput. Biol. Med. 2020, 120, 103764.
21. Liu, Y.; Munteanu, C. R.; Yan, Q.; Pedreira, N.; Kang, J.; Tang, S.; Zhou, C.; He, Z.; Tan, Z., Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats. PeerJ 2019, 7, e7840.
22. Munteanu, C. R.; Gestal, M.; Martinez-Acevedo, Y. G.; Pedreira, N.; Pazos, A.; Dorado, J., Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning. Int J Mol Sci 2019, 20.
23. Liu, Y.; Munteanu, C. R.; Kong, Z.; Ran, T.; Sahagun-Ruiz, A.; He, Z.; Zhou, C.; Tan, Z., Identification of coenzyme-binding proteins with machine learning algorithms. Comput Biol Chem 2019, 79, 185-192.
24. Mato-Abad, V.; Labiano-Fontcuberta, A.; Rodriguez-Yanez, S.; Garcia-Vazquez, R.; Munteanu, C. R.; Andrade-Garda, J.; Domingo-Santos, A.; Galan Sanchez-Seco, V.; Aladro, Y.; Martinez-Gines, M. L.; Ayuso, L.; Benito-Leon, J., Classification of radiologically isolated syndrome and clinically isolated syndrome with machine-learning techniques. Eur. J. Neurol. 2019, 26, 1000-1005.
25. Tsionou, M. I.; Knapp, C. E.; Foley, C. A.; Munteanu, C. R.; Cakebread, A.; Imberti, C.; Eykyn, T. R.; Young, J. D.; Paterson, B. M.; Blower, P. J.; Ma, M. T., Comparison of macrocyclic and acyclic chelators for gallium-68 radiolabelling. RSC advances 2017, 7, 49586-49599.
26. Deng, Y.; Liu, Y.; Tang, S.; Zhou, C.; Han, X.; Xiao, W.; Pastur-Romay, L. A.; Vazquez-Naya, J. M.; Loureiro, J. P.; Munteanu, C. R.; Tan, Z., General Machine Learning Model, Review, and Experimental-Theoretic Study of Magnolol Activity in Enterotoxigenic Induced Oxidative Stress. Curr Top Med Chem 2017, 17, 2977-2988.
27. Perez-Rey, D.; Alonso-Calvo, R.; Paraiso-Medina, S.; Munteanu, C. R.; Garcia-Remesal, M., SNOMED2HL7: A tool to normalize and bind SNOMED CT concepts to the HL7 Reference Information Model. Comput. Methods Programs Biomed. 2017, 149, 1-9.
28. Fernandez-Lozano, C.; Gestal, M.; Munteanu, C. R.; Dorado, J.; Pazos, A., A methodology for the design of experiments in computational intelligence with multiple regression models. PeerJ 2016, 4, e2721.
29. Liu, Y.; Munteanu, C. R.; Fernandez Blanco, E.; Tan, Z.; Santos Del Riego, A.; Pazos, A., Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices. Mol Inform 2015, 34, 736-41.
30. Munteanu, C. R.; Fernandez, B., Erratum: "Accurate intermolecular ground-state potential-energy surfaces of the HCCH-He, Ne, and Ar van der Waals complexes" [J. Chem. Phys. 123, 014309 (2005)]. J. Chem. Phys. 2016, 144, 119901.
31. Jeliazkova, N.; Chomenidis, C.; Doganis, P.; Fadeel, B.; Grafstrom, R.; Hardy, B.; Hastings, J.; Hegi, M.; Jeliazkov, V.; Kochev, N.; Kohonen, P.; Munteanu, C. R.; Sarimveis, H.; Smeets, B.; Sopasakis, P.; Tsiliki, G.; Vorgrimmler, D.; Willighagen, E., The eNanoMapper database for nanomaterial safety information. Beilstein journal of nanotechnology 2015, 6, 1609-34.
32. Tsiliki, G.; Munteanu, C. R.; Seoane, J. A.; Fernandez-Lozano, C.; Sarimveis, H.; Willighagen, E. L., RRegrs: an R package for computer-aided model selection with multiple regression models. J Cheminform 2015, 7, 46.
33. Fernandez-Lozano, C.; Cuinas, R. F.; Seoane, J. A.; Fernandez-Blanco, E.; Dorado, J.; Munteanu, C. R., Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models. J. Theor. Biol. 2015, 384, 50-8.
34. Munteanu, C. R.; Suntharalingam, K., Advances in cobalt complexes as anticancer agents. Dalton transactions 2015, 44, 13796-808.
35. Munteanu, C. R.; Pimenta, A. C.; Fernandez-Lozano, C.; Melo, A.; Cordeiro, M. N.; Moreira, I. S., Solvent accessible surface area-based hot-spot detection methods for protein-protein and protein-nucleic acid interfaces. Journal of chemical information and modeling 2015, 55, 1077-86.
36. Hastings, J.; Jeliazkova, N.; Owen, G.; Tsiliki, G.; Munteanu, C. R.; Steinbeck, C.; Willighagen, E., eNanoMapper: harnessing ontologies to enable data integration for nanomaterial risk assessment. Journal of biomedical semantics 2015, 6, 10.
37. Fernandez-Lozano, C.; Fernandez-Blanco, E.; Dave, K.; Pedreira, N.; Gestal, M.; Dorado, J.; Munteanu, C. R., Improving enzyme regulatory protein classification by means of SVM-RFE feature selection. Mol Biosyst 2014, 10, 1063-71.
38. Fernandez-Lozano, C.; Gestal, M.; Pedreira-Souto, N.; Postelnicu, L.; Dorado, J.; Munteanu, C. R., Kernel-based feature selection techniques for transport proteins based on star graph topological indices. Curr Top Med Chem 2013, 13, 1681-91.
39. Aguiar-Pulido, V.; Gestal, M.; Cruz-Monteagudo, M.; Rabunal, J. R.; Dorado, J.; Munteanu, C. R., Evolutionary computation and QSAR research. Curr Comput Aided Drug Des 2013, 9, 206-25.
40. Munteanu, C. R.; Henriksen, C.; Felker, P. M.; Fernandez, B., He-, Ne-, and Ar-phosgene intermolecular potential energy surfaces. J Phys Chem A 2013, 117, 3835-43.
41. Aguiar-Pulido, V.; Gestal, M.; Fernandez-Lozano, C.; Rivero, D.; Munteanu, C. R., Applied computational techniques on schizophrenia using genetic mutations. Curr Top Med Chem 2013, 13, 675-84.
42. Munteanu, C. R.; Dorado, J.; Pazos, A., Artificial intelligence techniques in medicinal chemistry. Curr Top Med Chem 2013, 13, 525.
43. Seoane, J. A.; Aguiar-Pulido, V.; Munteanu, C. R.; Rivero, D.; Rabunal, J. R.; Dorado, J.; Pazos, A., Biomedical data integration in computational drug design and bioinformatics. Curr Comput Aided Drug Des 2013, 9, 108-17.
44. Munteanu, C. R.; Dorado, J.; Matei-Ilfoveanu, I.; Nita, S. A., Regulatory affairs issues and legal ontologies in drug development. Front Biosci (Elite Ed) 2013, 5, 446-60.
45. Fernandez-Blanco, E.; Aguiar-Pulido, V.; Munteanu, C. R.; Dorado, J., Random Forest classification based on star graph topological indices for antioxidant proteins. J. Theor. Biol. 2013, 317, 331-7.
46. Fernandez-Blanco, E.; Rivero, D.; Rabunal, J.; Dorado, J.; Pazos, A.; Munteanu, C. R., Automatic seizure detection based on star graph topological indices. J. Neurosci. Methods 2012, 209, 410-9.
47. de Sousa, M. M.; Munteanu, C. R.; Pazos, A.; Fonseca, N. A.; Camacho, R.; Magalhaes, A. L., Amino acid pair- and triplet-wise groupings in the interior of alpha-helical segments in proteins. J. Theor. Biol. 2011, 271, 136-44.
48. Aguiar-Pulido, V.; Seoane, J. A.; Rabunal, J. R.; Dorado, J.; Pazos, A.; Munteanu, C. R., Machine learning techniques for single nucleotide polymorphism--disease classification models in schizophrenia. Molecules (Basel, Switzerland) 2010, 15, 4875-89.
49. Vazquez-Naya, J. M.; Martinez-Romero, M.; Porto-Pazos, A. B.; Novoa, F.; Valladares-Ayerbes, M.; Pereira, J.; Munteanu, C. R.; Dorado, J., Ontologies of drug discovery and design for neurology, cardiology and oncology. Curr. Pharm. Des. 2010, 16, 2724-36.
50. Munteanu, C. R.; Fernandez-Blanco, E.; Seoane, J. A.; Izquierdo-Novo, P.; Rodriguez-Fernandez, J. A.; Prieto-Gonzalez, J. M.; Rabunal, J. R.; Pazos, A., Drug discovery and design for complex diseases through QSAR computational methods. Curr. Pharm. Des. 2010, 16, 2640-55.
51. Martinez-Romero, M.; Vazquez-Naya, J. M.; Rabunal, J. R.; Pita-Fernandez, S.; Macenlle, R.; Castro-Alvarino, J.; Lopez-Roses, L.; Ulla, J. L.; Martinez-Calvo, A. V.; Vazquez, S.; Pereira, J.; Porto-Pazos, A. B.; Dorado, J.; Pazos, A.; Munteanu, C. R., Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network. Curr Drug Metab 2010, 11, 347-68.
52. Munteanu, C. R.; Magalhaes, A. L., Prot-2S: a new python web tool for protein secondary structure studies. International journal of bioinformatics research and applications 2009, 5, 402-16.
53. Munteanu, C. R.; Fernandez, B., Accurate intermolecular ground-state potential-energy surfaces of the HCCH-He, Ne, and Ar van der Waals complexes. J. Chem. Phys. 2005, 123, 014309.
54. Munteanu, C. R.; Cacheiro, J. L.; Fernandez, B., Accurate intermolecular ground state potential of the Ar-N2 van der Waals complex. J. Chem. Phys. 2004, 121, 10419-25.
55. Munteanu, C. R.; Lopez Cacheiro, J.; Fernandez, B., Accurate intermolecular ground state potential of the Ne-N2 van der Waals complex. J. Chem. Phys. 2004, 120, 9104-12.
56. Munteanu, C. R.; Lopez Cacheiro, J.; Fernandez, B.; Makarewicz, J., The chlorobenzene-argon ground state intermolecular potential energy surface. J. Chem. Phys. 2004, 121, 1390-6.
57. Abeijon, P.; Garcia-Mera, X.; Caamano, O.; Yanez, M.; Lopez-Castro, E.; Romero-Duran, F. J.; Gonzalez-Diaz, H., Multi-Target Mining of Alzheimer Disease Proteome with Hansch's QSBR-Perturbation Theory and Experimental-Theoretic Study of New Thiophene Isosters of Rasagiline. Curr Drug Targets 2017, 18, 511-521.
58. Aguero-Chapin, G.; Antunes, A.; Ubeira, F. M.; Chou, K. C.; Gonzalez-Diaz, H., Comparative study of topological indices of macro/supramolecular RNA complex networks. Journal of chemical information and modeling 2008, 48, 2265-77.
59. Aguero-Chapin, G.; Gonzalez-Diaz, H.; de la Riva, G.; Rodriguez, E.; Sanchez-Rodriguez, A.; Podda, G.; Vazquez-Padron, R. I., MMM-QSAR recognition of ribonucleases without alignment: comparison with an HMM model and isolation from Schizosaccharomyces pombe, prediction, and experimental assay of a new sequence. Journal of chemical information and modeling 2008, 48, 434-48.
60. Aguero-Chapin, G.; Gonzalez-Diaz, H.; Molina, R.; Varona-Santos, J.; Uriarte, E.; Gonzalez-Diaz, Y., Novel 2D maps and coupling numbers for protein sequences. The first QSAR study of polygalacturonases; isolation and prediction of a novel sequence from Psidium guajava L. FEBS Lett. 2006, 580, 723-30.
61. Aguero-Chapin, G.; Varona-Santos, J.; de la Riva, G. A.; Antunes, A.; Gonzalez-Vlla, T.; Uriarte, E.; Gonzalez-Diaz, H., Alignment-free prediction of polygalacturonases with pseudofolding topological indices: experimental isolation from Coffea arabica and prediction of a new sequence. Journal of proteome research 2009, 8, 2122-8.
62. Aguiar-Pulido, V.; Munteanu, C. R.; Seoane, J. A.; Fernandez-Blanco, E.; Perez-Montoto, L. G.; Gonzalez-Diaz, H.; Dorado, J., Naive Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer. Mol Biosyst 2012, 8, 1716-22.
63. Alonso, N.; Caamano, O.; Romero-Duran, F. J.; Luan, F.; MN, D. S. C.; Yanez, M.; Gonzalez-Diaz, H.; Garcia-Mera, X., Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS chemical neuroscience 2013, 4, 1393-403.
64. Anadon, A. M.; Rodriguez, E.; Garate, M. T.; Cuellar, C.; Romaris, F.; Chivato, T.; Rodero, M.; Gonzalez-Diaz, H.; Ubeira, F. M., Diagnosing human anisakiasis: recombinant Ani s 1 and Ani s 7 allergens versus the UniCAP 100 fluorescence enzyme immunoassay. Clin Vaccine Immunol 2010, 17, 496-502.
65. Aranzamendi, E.; Arrasate, S.; Sotomayor, N.; Gonzalez-Diaz, H.; Lete, E., Chiral Bronsted Acid-Catalyzed Enantioselective alpha-Amidoalkylation Reactions: A Joint Experimental and Predictive Study. ChemistryOpen 2016, 5, 540-549.
66. Arrasate, S.; Duardo-Sanchez, A.; De Miguel Beriain, I.; Casabona, C. R.; Gonzalez-Diaz, H., New Experimental and Computational Tools for Drug Discovery: Medicinal Chemistry, Personalized Medicine, Ethical & Legal Issues - Part-V. Curr Top Med Chem 2018, 18, 2141-2142.
67. Barbolla, I.; Hernandez-Suarez, L.; Quevedo-Tumailli, V.; Nocedo-Mena, D.; Arrasate, S.; Dea-Ayuela, M. A.; Gonzalez-Diaz, H.; Sotomayor, N.; Lete, E., Palladium-mediated synthesis and biological evaluation of C-10b substituted Dihydropyrrolo[1,2-b]isoquinolines as antileishmanial agents. Eur. J. Med. Chem. 2021, 220, 113458.
68. Barreiro, E.; Munteanu, C. R.; Cruz-Monteagudo, M.; Pazos, A.; Gonzalez-Diaz, H., Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems. Scientific reports 2018, 8, 12340.
69. Basak, S. C.; Nayarisseri, A.; Gonzalez-Diaz, H.; Bonchev, D., Editorial (Thematic Issue: Chemoinformatics Models for Pharmaceutical Design, Part 1). Curr. Pharm. Des. 2016, 22, 5041-5042.
70. Basak, S. C.; Nayarisseri, A.; Gonzalez-Diaz, H.; Bonchev, D., Editorial (Thematic Issue: Chemoinformatics Models for Pharmaceutical Design, Part 2). Curr. Pharm. Des. 2016, 22, 5177-5178.
71. Bediaga, H.; Arrasate, S.; Gonzalez-Diaz, H., PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer. ACS combinatorial science 2018, 20, 621-632.
72. Blay, V.; Yokoi, T.; Gonzalez-Diaz, H., Perturbation Theory-Machine Learning Study of Zeolite Materials Desilication. Journal of chemical information and modeling 2018.
73. Blay, V.; Yokoi, T.; Gonzalez-Diaz, H., Perturbation Theory-Machine Learning Study of Zeolite Materials Desilication. Journal of chemical information and modeling 2018, 58, 2414-2419.
74. Cabrera-Andrade, A.; Lopez-Cortes, A.; Jaramillo-Koupermann, G.; Gonzalez-Diaz, H.; Pazos, A.; Munteanu, C. R.; Perez-Castillo, Y.; Tejera, E., A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing. Pharmaceuticals (Basel) 2020, 13.
75. Cabrera-Andrade, A.; Lopez-Cortes, A.; Jaramillo-Koupermann, G.; Paz, Y. M. C.; Perez-Castillo, Y.; Munteanu, C. R.; Gonzalez-Diaz, H.; Pazos, A.; Tejera, E., Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis. Int J Mol Sci 2020, 21.
76. Cabrera-Andrade, A.; Lopez-Cortes, A.; Munteanu, C. R.; Pazos, A.; Perez-Castillo, Y.; Tejera, E.; Arrasate, S.; Gonzalez-Diaz, H., Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds. ACS omega 2020, 5, 27211-27220.
77. Carracedo-Reboredo, P.; Corona, R.; Martinez-Nunes, M.; Fernandez-Lozano, C.; Tsiliki, G.; Sarimveis, H.; Aranzamendi, E.; Arrasate, S.; Sotomayor, N.; Lete, E.; Munteanu, C. R.; Gonzalez-Diaz, H., MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry. Curr Top Med Chem 2020, 20, 305-317.
78. Casanola-Martin, G. M.; Le-Thi-Thu, H.; Perez-Gimenez, F.; Marrero-Ponce, Y.; Merino-Sanjuan, M.; Abad, C.; Gonzalez-Diaz, H., Multi-output model with Box-Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin-proteasome pathway. Mol. Divers. 2015, 19, 347-56.
79. Casanola-Martin, G. M.; Le-Thi-Thu, H.; Perez-Gimenez, F.; Marrero-Ponce, Y.; Merino-Sanjuan, M.; Abad, C.; Gonzalez-Diaz, H., Multi-output Model with Box-Jenkins Operators of Quadratic Indices for Prediction of Malaria and Cancer Inhibitors Targeting Ubiquitin- Proteasome Pathway (UPP) Proteins. Curr Protein Pept Sci 2016, 17, 220-7.
80. Concu, R.; Dea-Ayuela, M. A.; Perez-Montoto, L. G.; Bolas-Fernandez, F.; Prado-Prado, F. J.; Podda, G.; Uriarte, E.; Ubeira, F. M.; Gonzalez-Diaz, H., Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins. Journal of proteome research 2009, 8, 4372-82.
81. Concu, R.; Dea-Ayuela, M. A.; Perez-Montoto, L. G.; Prado-Prado, F. J.; Uriarte, E.; Bolas-Fernandez, F.; Podda, G.; Pazos, A.; Munteanu, C. R.; Ubeira, F. M.; Gonzalez-Diaz, H., 3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites. Biochimica et biophysica acta 2009, 1794, 1784-94.
82. Concu, R.; MN, D. S. C.; Munteanu, C. R.; Gonzalez-Diaz, H., PTML Model of Enzyme Subclasses for Mining the Proteome of Biofuel Producing Microorganisms. Journal of proteome research 2019, 18, 2735-2746.
83. Concu, R.; Podda, G.; Gonzalez-Diaz, H.; Shen, B., Review of computer-aided models for predicting collagen stability. Curr Comput Aided Drug Des 2011, 7, 287-303.
84. Concu, R.; Podda, G.; Ubeira, F. M.; Gonzalez-Diaz, H., Review of QSAR models for enzyme classes of drug targets: Theoretical background and applications in parasites, hosts, and other organisms. Curr. Pharm. Des. 2010, 16, 2710-23.
85. Concu, R.; Podda, G.; Uriarte, E.; Gonzalez-Diaz, H., Computational chemistry study of 3D-structure-function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials. J. Comput. Chem. 2009, 30, 1510-20.
86. Cruz-Monteagudo, M.; Gonzalez-Diaz, H., Unified drug-target interaction thermodynamic Markov model using stochastic entropies to predict multiple drugs side effects. Eur. J. Med. Chem. 2005, 40, 1030-41.
87. Cruz-Monteagudo, M.; Gonzalez-Diaz, H.; Aguero-Chapin, G.; Santana, L.; Borges, F.; Dominguez, E. R.; Podda, G.; Uriarte, E., Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems. J. Comput. Chem. 2007, 28, 1909-23.
88. Cruz-Monteagudo, M.; Gonzalez-Diaz, H.; Borges, F.; Dominguez, E. R.; Cordeiro, M. N., 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, 21, 619-32.
89. Cruz-Monteagudo, M.; Gonzalez-Diaz, H.; Borges, F.; Gonzalez-Diaz, Y., Simple stochastic fingerprints towards mathematical modeling in biology and medicine. 3. Ocular irritability classification model. Bull. Math. Biol. 2006, 68, 1555-72.
90. Cruz-Monteagudo, M.; Gonzalez-Diaz, H.; Uriarte, E., Simple stochastic fingerprints towards mathematical modeling in biology and medicine 2. Unifying Markov model for drugs side effects. Bull. Math. Biol. 2006, 68, 1527-54.
91. Cruz-Monteagudo, M.; Munteanu, C. R.; Borges, F.; Cordeiro, M. N.; Uriarte, E.; Gonzalez-Diaz, 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, 16, 9684-93.
92. Dea-Ayuela, M. A.; Perez-Castillo, Y.; Meneses-Marcel, A.; Ubeira, F. M.; Bolas-Fernandez, F.; Chou, K. C.; Gonzalez-Diaz, H., HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence. Bioorg. Med. Chem. 2008, 16, 7770-6.
93. Dieguez-Santana, K.; Casanola-Martin, G. M.; Green, J. R.; Rasulev, B.; Gonzalez-Diaz, H., Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models. Curr Top Med Chem 2021, 21, 819-827.
94. Dieguez-Santana, K.; Gonzalez-Diaz, H., Towards machine learning discovery of dual antibacterial drug-nanoparticle systems. Nanoscale 2021, 13, 17854-17870.
95. Diez-Alarcia, R.; Yanez-Perez, V.; Muneta-Arrate, I.; Arrasate, S.; Lete, E.; Meana, J. J.; Gonzalez-Diaz, H., Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [(35)S]GTPgammaS Binding Assays. ACS chemical neuroscience 2019, 10, 4476-4491.
96. Duardo-Sanchez, A.; Gonzalez-Diaz, H., Legal issues for chem-bioinformatics models. Front Biosci (Elite Ed) 2013, 5, 361-74.
97. Duardo-Sanchez, A.; Gonzalez-Diaz, H., Legal issues for chem-bioinformatics models. Front Biosci (Elite Ed) 2013, 5, 361-74.
98. Duardo-Sanchez, A.; Munteanu, C. R.; Riera-Fernandez, P.; Lopez-Diaz, A.; Pazos, A.; Gonzalez-Diaz, H., Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors. Journal of chemical information and modeling 2014, 54, 16-29.
99. Ferino, G.; Gonzalez-Diaz, H.; Delogu, G.; Podda, G.; Uriarte, E., Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome-disease relationships (QPDRs) and predicting prostate cancer. Biochem. Biophys. Res. Commun. 2008, 372, 320-5.
100. Fernandez-Lozano, C.; Gestal, M.; Gonzalez-Diaz, H.; Dorado, J.; Pazos, A.; Munteanu, C. R., Markov mean properties for cell death-related protein classification. J. Theor. Biol. 2014, 349, 12-21.
101. Ferreira da Costa, J.; Silva, D.; Caamano, O.; Brea, J. M.; Loza, M. I.; Munteanu, C. R.; Pazos, A.; Garcia-Mera, X.; Gonzalez-Diaz, H., Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics. ACS chemical neuroscience 2018, 9, 2572-2587.
102. Fiandaca, M. S.; Gonzalez-Dominguez, R.; Gonzalez-Diaz, H., New Experimental and Computational Tools for Drug Discovery: From Chemistry to Biology. Metabolomics, Pharmacokinetics, and Medicinal Chemistry. Part - IV. Curr Top Med Chem 2018, 18, 881-882.
103. Garcia, I.; Fall, Y.; Gomez, G.; Gonzalez-Diaz, 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, 15, 561-7.
104. Garcia, I.; Munteanu, C. R.; Fall, Y.; Gomez, G.; Uriarte, E.; Gonzalez-Diaz, H., QSAR and complex network study of the chiral HMGR inhibitor structural diversity. Bioorg. Med. Chem. 2009, 17, 165-75.
105. Garcia-Fuentes, M.; Gonzalez-Diaz, H.; Csaba, N., Nanocarriers & drug delivery: rational design and applications. Curr Top Med Chem 2014, 14, 1095-6.
106. Gia, O.; Marciani Magno, S.; Gonzalez-Diaz, H.; Quezada, E.; Santana, L.; Uriarte, E.; Dalla Via, L., Design, synthesis and photobiological properties of 3,4-cyclopentenepsoralens. Bioorg. Med. Chem. 2005, 13, 809-17.
107. Gomez, G.; Fall, Y.; Gonzalez-Diaz, H., New Experimental and Computational Tools for Drug Discovery. Part - IX. Curr Top Med Chem 2020, 20, 711-712.
108. Gonzalez-Diaz, H., Quantitative studies on Structure-Activity and Structure-Property Relationships (QSAR/QSPR). Curr Top Med Chem 2008, 8, 1554.
109. Gonzalez-Diaz, H., Network topological indices, drug metabolism, and distribution. Curr Drug Metab 2010, 11, 283-4.
110. Gonzalez-Diaz, H., QSAR and complex networks in pharmaceutical design, microbiology, parasitology, toxicology, cancer, and neurosciences. Curr. Pharm. Des. 2010, 16, 2598-600.
111. Gonzalez-Diaz, H., QSPR models for computer-aided drug design in microbiology, parasitology, and pharmacology. Curr Comput Aided Drug Des 2011, 7, 228-30.
112. Gonzalez-Diaz, H., Editorial: QSAR/QSPR models as enabling technologies for drug & targets discovery in: medicinal chemistry, microbiology-parasitology, neurosciences, bioinformatics, proteomics and other biomedical sciences. Curr Top Med Chem 2012, 12, 799-801.
113. Sampaio-Dias, I. E.; Rodriguez-Borges, J. E.; Yanez-Perez, V.; Arrasate, S.; Llorente, J.; Brea, J. M.; Bediaga, H.; Vina, D.; Loza, M. I.; Caamano, O.; Garcia-Mera, X.; Gonzalez-Diaz, H., Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML). ACS chemical neuroscience 2021, 12, 203-215.
114. Quevedo-Tumailli, V.; Ortega-Tenezaca, B.; Gonzalez-Diaz, H., IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds. Int J Mol Sci 2021, 22.
115. Ortega-Tenezaca, B.; Gonzalez-Diaz, H., IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks. Nanoscale 2021, 13, 1318-1330.
116. Munteanu, C. R.; Gutierrez-Asorey, P.; Blanes-Rodriguez, M.; Hidalgo-Delgado, I.; Blanco Liverio, M. J.; Castineiras Galdo, B.; Porto-Pazos, A. B.; Gestal, M.; Arrasate, S.; Gonzalez-Diaz, H., Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning. Int J Mol Sci 2021, 22.
117. Larrea-Sebal, A.; Benito-Vicente, A.; Fernandez-Higuero, J. A.; Jebari-Benslaiman, S.; Galicia-Garcia, U.; Uribe, K. B.; Cenarro, A.; Ostolaza, H.; Civeira, F.; Arrasate, S.; Gonzalez-Diaz, H.; Martin, C., MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants. JACC. Basic to translational science 2021, 6, 815-827.
118. Gonzalez-Diaz, H., New Experimental and Computational Tools for Drug Discovery: Part - XI. Curr Top Med Chem 2021, 21, 597-598.
119. Gonzalez-Diaz, H., New Experimental and Computational Tools for Drug Discovery. Part - XII. Curr Top Med Chem 2021, 21, 789.
120. Urista, D. V.; Carrue, D. B.; Otero, I.; Arrasate, S.; Quevedo-Tumailli, V. F.; Gestal, M.; Gonzalez-Diaz, H.; Munteanu, C. R., Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models. Biology 2020, 9.
121. Scotti, M. T.; Muratov, E. N.; Gonzalez-Diaz, H., New Experimental and Computational Tools for Drug Discovery - Part-VIII. Curr Top Med Chem 2020, 20, 277-279.
122. Santana, R.; Zuluaga, R.; Ganan, P.; Arrasate, S.; Onieva, E.; Montemore, M. M.; Gonzalez-Diaz, H., PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Cotherapy. Mol Pharm 2020, 17, 2612-2627.
123. Santana, R.; Zuluaga, R.; Ganan, P.; Arrasate, S.; Onieva, E.; Gonzalez-Diaz, H., Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models. Nanoscale 2020, 12, 13471-13483.
124. Santana, R.; Zuluaga, R.; Ganan, P.; Arrasate, S.; Onieva Caracuel, E.; Gonzalez-Diaz, H., PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives. ACS combinatorial science 2020, 22, 129-141.
125. Ortega-Tenezaca, B.; Quevedo-Tumailli, V.; Bediaga, H.; Collados, J.; Arrasate, S.; Madariaga, G.; Munteanu, C. R.; Cordeiro, M.; Gonzalez-Diaz, H., PTML Multi-Label Algorithms: Models, Software, and Applications. Curr Top Med Chem 2020, 20, 2326-2337.
126. Montemore, M. M.; Santana, R.; Fall, Y.; Gonzalez-Diaz, H., New Experimental and Computational Tools for Drug Discovery. From Old Way to New Series - Part-X. Curr Top Med Chem 2020, 20, 2279-2280.
127. Lopez-Cortes, A.; Paz, Y. M. C.; Guerrero, S.; Cabrera-Andrade, A.; Barigye, S. J.; Munteanu, C. R.; Gonzalez-Diaz, H.; Pazos, A.; Perez-Castillo, Y.; Tejera, E., OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine. Scientific reports 2020, 10, 5285.
128. Vasquez-Dominguez, E.; Armijos-Jaramillo, V. D.; Tejera, E.; Gonzalez-Diaz, H., Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds. Mol Pharm 2019, 16, 4200-4212.
129. Tenorio-Borroto, E.; Castanedo, N.; Garcia-Mera, X.; Rivadeneira, K.; Vazquez Chagoyan, J. C.; Barbabosa Pliego, A.; Munteanu, C. R.; Gonzalez-Diaz, H., Perturbation Theory Machine Learning Modeling of Immunotoxicity for Drugs Targeting Inflammatory Cytokines and Study of the Antimicrobial G1 Using Cytometric Bead Arrays. Chem. Res. Toxicol. 2019, 32, 1811-1823.
130. Scotti, M. T.; Muratov, E. N.; Gonzalez-Diaz, H., New Experimental and Computational Tools for Drug Discovery. - Part-VII. Curr Top Med Chem 2019, 19, 898-899.
131. Santana, R.; Zuluaga, R.; Ganan, P.; Arrasate, S.; Onieva, E.; Gonzalez-Diaz, H., Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models. Nanoscale 2019, 11, 21811-21823.
132. Nocedo-Mena, D.; Cornelio, C.; Camacho-Corona, M. D. R.; Garza-Gonzalez, E.; Waksman de Torres, N.; Arrasate, S.; Sotomayor, N.; Lete, E.; Gonzalez-Diaz, H., Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks. Journal of chemical information and modeling 2019, 59, 1109-1120.
133. Gonzalez-Durruthy, M.; Monserrat, J. M.; Viera de Oliveira, P.; Fagan, S. B.; Werhli, A. V.; Machado, K.; Melo, A.; Gonzalez-Diaz, H.; Concu, R.; MN, D. S. C., Computational MitoTarget Scanning Based on Topological Vacancies of Single-Walled Carbon Nanotubes with the Human Mitochondrial Voltage-Dependent Anion Channel (hVDAC1). Chem. Res. Toxicol. 2019, 32, 566-577.
134. Gonzalez-Durruthy, M.; Manske Nunes, S.; Ventura-Lima, J.; Gelesky, M. A.; Gonzalez-Diaz, H.; Monserrat, J. M.; Concu, R.; Cordeiro, M., MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors. Journal of chemical information and modeling 2019, 59, 86-97.
135. Ambure, P.; Halder, A. K.; Gonzalez Diaz, H.; Cordeiro, M., QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models. Journal of chemical information and modeling 2019, 59, 2538-2544.
136. Simon-Vidal, L.; Garcia-Calvo, O.; Oteo, U.; Arrasate, S.; Lete, E.; Sotomayor, N.; Gonzalez-Diaz, H., Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies. Journal of chemical information and modeling 2018, 58, 1384-1396.
137. Quevedo-Tumailli, V. F.; Ortega-Tenezaca, B.; Gonzalez-Diaz, H., Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome. Journal of proteome research 2018, 17, 1258-1268.
138. Monteagudo, M. C.; Gonzalez-Diaz, H., New Experimental and Computational Tools for Drug Discovery: Medicinal Chemistry, Molecular Docking, and Machine Learning - Part-VI. Curr Top Med Chem 2018, 18, 2325-2326.
139. Lopez-Cortes, A.; Paz, Y. M. C.; Cabrera-Andrade, A.; Barigye, S. J.; Munteanu, C. R.; Gonzalez-Diaz, H.; Pazos, A.; Perez-Castillo, Y.; Tejera, E., Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis. Scientific reports 2018, 8, 16679.
140. Vazquez-Prieto, S.; Paniagua, E.; Solana, H.; Ubeira, F. M.; Gonzalez-Diaz, H., A study of the Immune Epitope Database for some fungi species using network topological indices. Mol. Divers. 2017, 21, 713-718.
141. Sylla-Iyarreta Veitia, M.; Dumas, F.; Gonzalez-Diaz, H., Editorial: New Experimental and Computational Tools for Drug Discovery: From Chemistry to Biology. Part-II. Curr Top Med Chem 2017, 17, 2901-2902.
142. Shameer, K.; Nayarisseri, A.; Romero Duran, F. X.; Gonzalez-Diaz, H., Editorial: Improving Neuropharmacology using Big Data, Machine Learning and Computational Algorithms. Current neuropharmacology 2017, 15, 1058-1061.
143. Martinez-Arzate, S. G.; Tenorio-Borroto, E.; Barbabosa Pliego, A.; Diaz-Albiter, H. M.; Vazquez-Chagoyan, J. C.; Gonzalez-Diaz, H., PTML Model for Proteome Mining of B-Cell Epitopes and Theoretical-Experimental Study of Bm86 Protein Sequences from Colima, Mexico. Journal of proteome research 2017, 16, 4093-4103.
144. Liu, Y.; Munteanu, C. R.; Fernandez-Lozano, C.; Pazos, A.; Ran, T.; Tan, Z.; Yu, Y.; Zhou, C.; Tang, S.; Gonzalez-Diaz, H., Experimental Study and ANN Dual-Time Scale Perturbation Model of Electrokinetic Properties of Microbiota. Frontiers in microbiology 2017, 8, 1216.
145. Gonzalez-Durruthy, M.; Werhli, A. V.; Seus, V.; Machado, K. S.; Pazos, A.; Munteanu, C. R.; Gonzalez-Diaz, H.; Monserrat, J. M., Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory. Scientific reports 2017, 7, 13271.
146. Gonzalez-Durruthy, M.; Monserrat, J. M.; Rasulev, B.; Casanola-Martin, G. M.; Barreiro Sorrivas, J. M.; Paraiso-Medina, S.; Maojo, V.; Gonzalez-Diaz, H.; Pazos, A.; Munteanu, C. R., Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra. Nanomaterials 2017, 7.
147. Gonzalez-Durruthy, M.; Alberici, L. C.; Curti, C.; Naal, Z.; Atique-Sawazaki, D. T.; Vazquez-Naya, J. M.; Gonzalez-Diaz, H.; Munteanu, C. R., Experimental-Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov-Shannon Entropy Invariants. Journal of chemical information and modeling 2017, 57, 1029-1044.
148. Todeschini, R.; Pazos, A.; Arrasate, S.; Gonzalez-Diaz, H., Data Analysis in Chemistry and Bio-Medical Sciences. Int J Mol Sci 2016, 17.
149. Tenorio-Borroto, E.; Ramirez, F. R.; Vazquez Chagoyan, J. C.; de Oca Jimenez, R. M.; Garcia-Mera, X.; Gonzalez-Diaz, H., Experimental-Theoretic Approach to Drug-Lymphocyte Interactome Networks with Flow Cytometry and Spectral Moments Perturbation Theory. Curr. Pharm. Des. 2016, 22, 5114-5119.
150. Romero-Duran, F. J.; Alonso, N.; Yanez, M.; Caamano, O.; Garcia-Mera, X.; Gonzalez-Diaz, H., Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology 2016, 103, 270-8.
151. Ran, T.; Liu, Y.; Li, H.; Tang, S.; He, Z.; Munteanu, C. R.; Gonzalez-Diaz, H.; Tan, Z.; Zhou, C., Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learning Model Based on Perturbation Theory. Scientific reports 2016, 6, 30174.
152. Messina, P. V.; Besada-Porto, J. M.; Rial, R.; Gonzalez-Diaz, H.; Ruso, J. M., Computational Modeling and Experimental Facts of Mixed Self- Assembly Systems. Curr. Pharm. Des. 2016, 22, 5249-5256.
153. Gonzalez-Diaz, H., ADMET-Multi-Output Cheminformatics Models for Drug Delivery, Interactomics, and Nanotoxicology. Curr Drug Deliv 2016.
154. Munteanu, C. R.; Gonzalez-Diaz, H.; Garcia, R.; Loza, M.; Pazos, A., Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models. Comb Chem High Throughput Screen 2015, 18, 735-50.
155. Messina, P. V.; Besada-Porto, J. M.; Gonzalez-Diaz, H.; Ruso, J. M., Self-Assembled Binary Nanoscale Systems: Multioutput Model with LFER-Covariance Perturbation Theory and an Experimental-Computational Study of NaGDC-DDAB Micelles. Langmuir 2015, 31, 12009-18.
156. Liu, Y.; Buendia-Rodriguez, G.; Penuelas-Rivas, C. G.; Tan, Z.; Rivas-Guevara, M.; Tenorio-Borroto, E.; Munteanu, C. R.; Pazos, A.; Gonzalez-Diaz, H., Experimental and computational studies of fatty acid distribution networks. Mol Biosyst 2015, 11, 2964-77.
157. Herrera-Ibata, D. M.; Pazos, A.; Orbegozo-Medina, R. A.; Romero-Duran, F. J.; Gonzalez-Diaz, H., Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties. Biosystems 2015, 132-133, 20-34.
158. Vergara-Galicia, J.; Prado-Prado, F. J.; Gonzalez-Diaz, H., Galvez-Markov network transferability indices: review of classic theory and new model for perturbations in metabolic reactions. Curr Drug Metab 2014, 15, 557-64.
159. Vazquez-Prieto, S.; Gonzalez-Diaz, H.; Paniagua, E.; Vilas, R.; Ubeira, F. M., A QSPR-like model for multilocus genotype networks of Fasciola hepatica in Northwest Spain. J. Theor. Biol. 2014, 343, 16-24.
160. Tenorio-Borroto, E.; Ramirez, F. R.; Speck-Planche, A.; Cordeiro, M. N.; Luan, F.; Gonzalez-Diaz, H., QSPR and flow cytometry analysis (QSPR-FCA): review and new findings on parallel study of multiple interactions of chemical compounds with immune cellular and molecular targets. Curr Drug Metab 2014, 15, 414-28.
161. Tenorio-Borroto, E.; Penuelas-Rivas, C. G.; Vasquez-Chagoyan, J. C.; Castanedo, N.; Prado-Prado, F. J.; Garcia-Mera, X.; Gonzalez-Diaz, H., Model for high-throughput screening of drug immunotoxicity--study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur. J. Med. Chem. 2014, 72, 206-20.
162. Romero Duran, F. J.; Alonso, N.; Caamano, O.; Garcia-Mera, X.; Yanez, M.; Prado-Prado, F. J.; Gonzalez-Diaz, H., Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates. Int J Mol Sci 2014, 15, 17035-64.
163. Munteanu, C. R.; Pedreira, N.; Dorado, J.; Pazos, A.; Perez-Montoto, L. G.; Ubeira, F. M.; Gonzalez-Diaz, 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, 33, 276-85.
164. Luan, F.; Kleandrova, V. V.; Gonzalez-Diaz, H.; Ruso, J. M.; Melo, A.; Speck-Planche, A.; Cordeiro, M. N., Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. Nanoscale 2014, 6, 10623-30.
165. Kleandrova, V. V.; Luan, F.; Gonzalez-Diaz, H.; Ruso, J. M.; Speck-Planche, A.; Cordeiro, M. N., Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. Environ. Sci. Technol. 2014, 48, 14686-94.
166. Kleandrova, V. V.; Luan, F.; Gonzalez-Diaz, H.; Ruso, J. M.; Melo, A.; Speck-Planche, A.; Cordeiro, M. N., Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ Int 2014, 73, 288-94.
167. Gonzalez-Diaz, H.; Speck-Planche, A.; Cordeiro, M. N., Editorial: Chemoinformatics in metabolomics, modeling chemical reactivity and ADMET processes part 1. Curr Drug Metab 2014, 15, 345.
168. Gonzalez-Diaz, H.; Speck-Planche, A.; Cordeiro, M. N., Chemoinformatics in metabolomics, from molecular mechanics, dynamics, and docking to complex metabolic networks, part 2. Curr Drug Metab 2014, 15, 489.
169. Gonzalez-Diaz, H.; Perez-Montoto, L. G.; Ubeira, F. M., Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms. Journal of immunology research 2014, 2014, 768515.
170. Gonzalez-Diaz, H.; Herrera-Ibata, D. M.; Duardo-Sanchez, A.; Munteanu, C. R.; Orbegozo-Medina, R. A.; Pazos, A., ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks. Journal of chemical information and modeling 2014, 54, 744-55.
171. Gonzalez-Diaz, H.; Arrasate, S.; Juan, A. G.; Sotomayor, N.; Lete, E.; Speck-Planche, A.; Ruso, J. M.; Luan, F.; Cordeiro, M. N., Matrix trace operators: from spectral moments of molecular graphs and complex networks to perturbations in synthetic reactions, micelle nanoparticles, and drug ADME processes. Curr Drug Metab 2014, 15, 470-88.
172. Tenorio-Borroto, E.; Garcia-Mera, X.; Penuelas-Rivas, C. G.; Vasquez-Chagoyan, J. C.; Prado-Prado, F. J.; Castanedo, N.; Gonzalez-Diaz, H., Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity. Curr Top Med Chem 2013, 13, 1636-49.
173. Sobarzo-Sanchez, E.; Bilbao-Ramos, P.; Dea-Ayuela, M.; Gonzalez-Diaz, H.; Yanez, M.; Uriarte, E.; Santana, L.; Martinez-Sernandez, V.; Bolas-Fernandez, F.; Ubeira, F. M., Synthetic oxoisoaporphine alkaloids: in vitro, in vivo and in silico assessment of antileishmanial activities. PLoS ONE 2013, 8, e77560.
174. Prado-Prado, F.; Garcia-Mera, X.; Rodriguez-Borges, J. E.; Concu, R.; Perez-Montoto, L. G.; Gonzalez-Diaz, H.; Duardo-Sanchez, A., Patents of bio-active compounds based on computer-aided drug discovery techniques. Front Biosci (Elite Ed) 2013, 5, 399-407.
175. Luan, F.; Cordeiro, M. N.; Alonso, N.; Garcia-Mera, X.; Caamano, O.; Romero-Duran, F. J.; Yanez, M.; Gonzalez-Diaz, H., TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases. Bioorg. Med. Chem. 2013, 21, 1870-9.
176. Gonzalez-Diaz, H.; Riera-Fernandez, P.; Pazos, A.; Munteanu, C. R., The Rucker-Markov invariants of complex Bio-Systems: applications in Parasitology and Neuroinformatics. Biosystems 2013, 111, 199-207.
177. Gonzalez-Diaz, H.; Arrasate, S.; Sotomayor, N.; Lete, E.; Munteanu, C. R.; Pazos, A.; Besada-Porto, L.; Ruso, J. M., MIANN models in medicinal, physical and organic chemistry. Curr Top Med Chem 2013, 13, 619-41.
178. Gonzalez-Diaz, H.; Arrasate, S.; Gomez-SanJuan, A.; Sotomayor, N.; Lete, E.; Besada-Porto, L.; Ruso, J. M., General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry. Curr Top Med Chem 2013, 13, 1713-41.
179. Gonzalez-Diaz, H., Computational prediction of drug-target interactions in medicinal chemistry. Curr Top Med Chem 2013, 13, 1619-21.
180. Tenorio-Borroto, E.; Penuelas-Rivas, C. G.; Vasquez-Chagoyan, J. C.; Prado-Pradoa, F. J.; Garcia-Mera, X.; Gonzalez-Diaz, H., Immunotoxicity, flow cytometry, and chemoinformatics: review, bibliometric analysis, and new QSAR model of drug effects over macrophages. Curr Top Med Chem 2012, 12, 1815-33.
181. Tenorio-Borroto, E.; Penuelas Rivas, C. G.; Vasquez Chagoyan, J. C.; Castanedo, N.; Prado-Prado, F. J.; Garcia-Mera, X.; Gonzalez-Diaz, H., ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorg. Med. Chem. 2012, 20, 6181-94.
182. Riera-Fernandez, P.; Munteanu, C. R.; Escobar, M.; Prado-Prado, F.; Martin-Romalde, R.; Pereira, D.; Villalba, K.; Duardo-Sanchez, A.; Gonzalez-Diaz, H., New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks. J. Theor. Biol. 2012, 293, 174-88.
183. Riera-Fernandez, P.; Martin-Romalde, R.; Prado-Prado, F. J.; Escobar, M.; Munteanu, C. R.; Concu, R.; Duardo-Sanchez, A.; Gonzalez-Diaz, H., From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices. Curr Top Med Chem 2012, 12, 927-60.
184. Prado-Prado, F.; Garcia-Mera, X.; Escobar, M.; Alonso, N.; Caamano, O.; Yanez, M.; Gonzalez-Diaz, 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, 1843-65.
185. Gonzalez-Diaz, H.; Riera-Fernandez, P., New Markov-autocorrelation indices for re-evaluation of links in chemical and biological complex networks used in metabolomics, parasitology, neurosciences, and epidemiology. Journal of chemical information and modeling 2012, 52, 3331-40.
186. Gonzalez-Diaz, H.; Munteanu, C. R.; 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, 8, 851-62.
187. Riera-Fernandez, P.; Munteanu, C. R.; Dorado, J.; Martin-Romalde, R.; Duardo-Sanchez, A.; Gonzalez-Diaz, H., From chemical graphs in computer-aided drug design to general Markov-Galvez indices of drug-target, proteome, drug-parasitic disease, technological, and social-legal networks. Curr Comput Aided Drug Des 2011, 7, 315-37.
188. Prado-Prado, F.; Garcia-Mera, X.; Escobar, M.; Sobarzo-Sanchez, E.; Yanez, M.; Riera-Fernandez, P.; Gonzalez-Diaz, 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, 46, 5838-51.
189. Prado-Prado, F.; Garcia-Mera, X.; Abeijon, P.; Alonso, N.; Caamano, O.; Yanez, M.; Garate, T.; Mezo, M.; Gonzalez-Warleta, M.; Muino, L.; Ubeira, F. M.; Gonzalez-Diaz, 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, 46, 1074-94.
190. Marzaro, G.; Chilin, A.; Guiotto, A.; Uriarte, E.; Brun, P.; Castagliuolo, I.; Tonus, F.; Gonzalez-Diaz, H., Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. Eur. J. Med. Chem. 2011, 46, 2185-92.
191. Gonzalez-Diaz, H.; Prado-Prado, F.; Sobarzo-Sanchez, E.; Haddad, M.; Maurel Chevalley, S.; Valentin, A.; Quetin-Leclercq, J.; Dea-Ayuela, M. A.; Teresa Gomez-Munos, M.; Munteanu, C. R.; Jose Torres-Labandeira, J.; Garcia-Mera, X.; Tapia, R. A.; Ubeira, F. M., 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, 276, 229-49.
192. Gonzalez-Diaz, H.; Prado-Prado, F.; Garcia-Mera, X.; Alonso, N.; Abeijon, P.; Caamano, O.; Yanez, M.; Munteanu, C. R.; Pazos, A.; Dea-Ayuela, M. A.; Gomez-Munoz, M. T.; Garijo, M. M.; Sansano, J.; Ubeira, F. M., 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. Journal of proteome research 2011, 10, 1698-718.
193. Gonzalez-Diaz, H.; Muino, L.; Anadon, A. M.; Romaris, F.; Prado-Prado, F. J.; Munteanu, C. R.; Dorado, J.; Sierra, A. P.; Mezo, M.; Gonzalez-Warleta, M.; Garate, T.; Ubeira, F. M., 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, 7, 1938-55.
194. Rodriguez-Soca, Y.; Munteanu, C. R.; Dorado, J.; Pazos, A.; Prado-Prado, F. J.; Gonzalez-Diaz, H., Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions. Journal of proteome research 2010, 9, 1182-90.
195. Prado-Prado, F. J.; Ubeira, F. M.; Borges, F.; Gonzalez-Diaz, H., Unified QSAR & network-based computational chemistry approach to antimicrobials. II. Multiple distance and triadic census analysis of antiparasitic drugs complex networks. J. Comput. Chem. 2010, 31, 164-73.
196. Prado-Prado, F. J.; Garcia-Mera, X.; Gonzalez-Diaz, H., Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg. Med. Chem. 2010, 18, 2225-2231.
197. Gonzalez-Diaz, H.; Romaris, F.; Duardo-Sanchez, A.; Perez-Montoto, L. G.; Prado-Prado, F.; Patlewicz, G.; Ubeira, F. M., Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. Curr. Pharm. Des. 2010, 16, 2737-64.
198. Gonzalez-Diaz, H.; Duardo-Sanchez, A.; Ubeira, F. M.; Prado-Prado, F.; Perez-Montoto, L. G.; 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, 11, 379-406.
199. Gonzalez-Diaz, H.; Dea-Ayuela, M. A.; Perez-Montoto, L. G.; Prado-Prado, F. J.; Aguero-Chapin, G.; Bolas-Fernandez, F.; Vazquez-Padron, R. I.; Ubeira, F. M., QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein. Mol. Divers. 2010, 14, 349-69.
200. Vina, D.; Uriarte, E.; Orallo, F.; Gonzalez-Diaz, H., Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol Pharm 2009, 6, 825-35.
201. Vilar, S.; Gonzalez-Diaz, H.; Santana, L.; Uriarte, E., A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J. Theor. Biol. 2009, 261, 449-58.
202. Prado-Prado, F. J.; Uriarte, E.; Borges, F.; Gonzalez-Diaz, H., Multi-target spectral moments for QSAR and Complex Networks study of antibacterial drugs. Eur. J. Med. Chem. 2009, 44, 4516-21.
203. Prado-Prado, F. J.; Martinez de la Vega, O.; Uriarte, E.; Ubeira, F. M.; Chou, K. C.; Gonzalez-Diaz, H., Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg. Med. Chem. 2009, 17, 569-75.
204. Prado-Prado, F. J.; Borges, F.; Uriarte, E.; Perez-Montoto, L. G.; Gonzalez-Diaz, H., Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species. Anal. Chim. Acta 2009, 651, 159-64.
205. Prado-Prado, F. J.; Borges, F.; Perez-Montoto, L. G.; Gonzalez-Diaz, H., Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species. Eur. J. Med. Chem. 2009, 44, 4051-6.
206. Perez-Montoto, L. G.; Santana, L.; Gonzalez-Diaz, H., Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories. Eur. J. Med. Chem. 2009, 44, 4461-9.
207. Perez-Montoto, L. G.; Dea-Ayuela, M. A.; Prado-Prado, F. J.; Bolas-Fernandez, F.; Ubeira, F. M.; Gonzalez-Diaz, H., Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks. Polymer (Guildf) 2009, 50, 3857-3870.
208. Munteanu, C. R.; Vazquez, J. M.; Dorado, J.; Sierra, A. P.; Sanchez-Gonzalez, A.; Prado-Prado, F. J.; Gonzalez-Diaz, H., Complex network spectral moments for ATCUN motif DNA cleavage: first predictive study on proteins of human pathogen parasites. Journal of proteome research 2009, 8, 5219-28.
209. Munteanu, C. R.; Magalhaes, A. L.; Uriarte, E.; Gonzalez-Diaz, H., Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices. J. Theor. Biol. 2009, 257, 303-11.
210. Gonzalez-Diaz, H.; Perez-Montoto, L. G.; Duardo-Sanchez, A.; Paniagua, E.; Vazquez-Prieto, S.; Vilas, R.; Dea-Ayuela, M. A.; Bolas-Fernandez, F.; Munteanu, C. R.; Dorado, J.; Costas, J.; Ubeira, F. M., Generalized lattice graphs for 2D-visualization of biological information. J. Theor. Biol. 2009, 261, 136-47.
211. Gonzalez-Diaz, H.; Prado-Prado, F.; Ubeira, F. M., Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. Curr Top Med Chem 2008, 8, 1676-90.
212. Perez-Bello, A.; Munteanu, C. R.; Ubeira, F. M.; De Magalhaes, A. L.; Uriarte, E.; Gonzalez-Diaz, H., Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices. J. Theor. Biol. 2009, 256, 458-66.
213. Santana, L.; Gonzalez-Diaz, H.; Quezada, E.; Uriarte, E.; Yanez, M.; Vina, D.; Orallo, F., Quantitative structure-activity relationship and complex network approach to monoamine oxidase A and B inhibitors. Journal of medicinal chemistry 2008, 51, 6740-51.
214. Munteanu, C. R.; Gonzalez-Diaz, H.; Borges, F.; de Magalhaes, A. L., Natural/random protein classification models based on star network topological indices. J. Theor. Biol. 2008, 254, 775-83.
215. Munteanu, C. R.; Gonzalez-Diaz, H.; Magalhaes, A. L., Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices. J. Theor. Biol. 2008, 254, 476-82.
216. Prado-Prado, F. J.; Gonzalez-Diaz, H.; de la Vega, O. M.; Ubeira, F. M.; Chou, K. C., Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg. Med. Chem. 2008, 16, 5871-80.
217. Vilar, S.; Gonzalez-Diaz, H.; Santana, L.; Uriarte, E., QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks. J. Comput. Chem. 2008, 29, 2613-22.
218. Gonzalez-Diaz, H.; Gonzalez-Diaz, Y.; Santana, L.; Ubeira, F. M.; Uriarte, E., Proteomics, networks and connectivity indices. Proteomics 2008, 8, 750-78.
219. Gonzalez-Diaz, H.; Prado-Prado, F. J., Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: multispecies activity models for antifungals. J. Comput. Chem. 2008, 29, 656-67.
220. Gonzalez-Diaz, H.; Vilar, S.; Santana, L.; Uriarte, E., Medicinal chemistry and bioinformatics--current trends in drugs discovery with networks topological indices. Curr Top Med Chem 2007, 7, 1015-29.
221. Gonzalez-Diaz, H.; Perez-Castillo, Y.; Podda, G.; Uriarte, E., Computational chemistry comparison of stable/nonstable protein mutants classification models based on 3D and topological indices. J. Comput. Chem. 2007, 28, 1990-5.
222. Gonzalez-Diaz, H.; Vilar, S.; Santana, L.; Podda, G.; Uriarte, E., On the applicability of QSAR for recognition of miRNA bioorganic structures at early stages of organism and cell development: embryo and stem cells. Bioorg. Med. Chem. 2007, 15, 2544-50.
223. Gonzalez-Diaz, H.; Aguero-Chapin, G.; Varona, J.; Molina, R.; Delogu, G.; Santana, L.; Uriarte, E.; Podda, G., 2D-RNA-coupling numbers: a new computational chemistry approach to link secondary structure topology with biological function. J. Comput. Chem. 2007, 28, 1049-56.
224. Gonzalez-Diaz, H.; Saiz-Urra, L.; Molina, R.; Santana, L.; Uriarte, E., A model for the recognition of protein kinases based on the entropy of 3D van der Waals interactions. Journal of proteome research 2007, 6, 904-8.
225. Gonzalez-Diaz, H.; Saiz-Urra, L.; Molina, R.; Gonzalez-Diaz, Y.; Sanchez-Gonzalez, A., Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments. J. Comput. Chem. 2007, 28, 1042-8.
226. Gonzalez-Diaz, H.; Bonet, I.; Teran, C.; De Clercq, E.; Bello, R.; Garcia, M. M.; Santana, L.; Uriarte, E., ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. Eur. J. Med. Chem. 2007, 42, 580-5.
227. Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte, E., Unified QSAR approach to antimicrobials. Part 2: predicting activity against more than 90 different species in order to halt antibacterial resistance. Bioorg. Med. Chem. 2007, 15, 897-902.
228. Gonzalez-Diaz, H.; Olazabal, E.; Santana, L.; Uriarte, E.; Gonzalez-Diaz, Y.; Castanedo, N., QSAR study of anticoccidial activity for diverse chemical compounds: prediction and experimental assay of trans-2-(2-nitrovinyl)furan. Bioorg. Med. Chem. 2007, 15, 962-8.
229. Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E., Unify QSAR approach to antimicrobials. Part 1: predicting antifungal activity against different species. Bioorg. Med. Chem. 2006, 14, 5973-80.
230. Gonzalez-Diaz, H.; Sanchez-Gonzalez, A.; Gonzalez-Diaz, Y., 3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif. J. Inorg. Biochem. 2006, 100, 1290-7.
231. Santana, L.; Uriarte, E.; Gonzalez-Diaz, H.; Zagotto, G.; Soto-Otero, R.; Mendez-Alvarez, E., A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins. Journal of medicinal chemistry 2006, 49, 1149-56.
232. Gonzalez-Diaz, H.; Perez-Bello, A.; Uriarte, E.; Gonzalez-Diaz, Y., QSAR study for mycobacterial promoters with low sequence homology. Bioorg. Med. Chem. Lett. 2006, 16, 547-53.
233. Gonzalez-Diaz, H.; Vina, D.; Santana, L.; de Clercq, E.; Uriarte, E., Stochastic entropy QSAR for the in silico discovery of anticancer compounds: prediction, synthesis, and in vitro assay of new purine carbanucleosides. Bioorg. Med. Chem. 2006, 14, 1095-107.
234. Gonzalez-Diaz, H.; Uriarte, E., Proteins QSAR with Markov average electrostatic potentials. Bioorg. Med. Chem. Lett. 2005, 15, 5088-94.
235. Gonzalez-Diaz, H.; Molina, R.; Uriarte, E., Recognition of stable protein mutants with 3D stochastic average electrostatic potentials. FEBS Lett. 2005, 579, 4297-301.
236. Gonzalez-Diaz, H.; Aguero-Chapin, G.; Varona-Santos, J.; Molina, R.; de la Riva, G.; Uriarte, E., 2D RNA-QSAR: assigning ACC oxidase family membership with stochastic molecular descriptors; isolation and prediction of a sequence from Psidium guajava L. Bioorg. Med. Chem. Lett. 2005, 15, 2932-7.
237. Saiz-Urra, L.; Gonzalez-Diaz, H.; Uriarte, E., Proteins Markovian 3D-QSAR with spherically-truncated average electrostatic potentials. Bioorg. Med. Chem. 2005, 13, 3641-7.
238. Helguera, A. M.; Cabrera Perez, M. A.; Gonzalez, M. P.; Ruiz, R. M.; Gonzalez Diaz, H., A topological substructural approach applied to the computational prediction of rodent carcinogenicity. Bioorg. Med. Chem. 2005, 13, 2477-88.
239. Gonzalez-Diaz, H.; Cruz-Monteagudo, M.; Vina, D.; Santana, L.; Uriarte, E.; De Clercq, E., QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices. Bioorg. Med. Chem. Lett. 2005, 15, 1651-7.
240. Gonzalez-Diaz, H.; Torres-Gomez, L. A.; Guevara, Y.; Almeida, M. S.; Molina, R.; Castanedo, 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, 11, 116-23.
241. Gonzalez-Diaz, H.; Tenorio, E.; Castanedo, N.; Santana, L.; Uriarte, E., 3D QSAR Markov model for drug-induced eosinophilia--theoretical prediction and preliminary experimental assay of the antimicrobial drug G1. Bioorg. Med. Chem. 2005, 13, 1523-30.
242. Gonzalez-Diaz, H.; Cruz-Monteagudo, M.; Molina, R.; Tenorio, E.; Uriarte, E., Predicting multiple drugs side effects with a general drug-target interaction thermodynamic Markov model. Bioorg. Med. Chem. 2005, 13, 1119-29.
243. Gonzalez-Diaz, H.; Aguero, G.; Cabrera, M. A.; Molina, R.; Santana, L.; Uriarte, E.; Delogu, G.; Castanedo, 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, 15, 551-7.
244. Gonzalez-Diaz, H.; Uriarte, E., Biopolymer stochastic moments. I. Modeling human rhinovirus cellular recognition with protein surface electrostatic moments. Biopolymers 2005, 77, 296-303.
245. Gonzalez-Diaz, H.; Uriarte, E.; Ramos de Armas, R., Predicting stability of Arc repressor mutants with protein stochastic moments. Bioorg. Med. Chem. 2005, 13, 323-31.
246. Marrero Ponce, Y.; Cabrera Perez, M. A.; Romero Zaldivar, V.; Gonzalez Diaz, H.; Torrens, F., A new topological descriptors based model for predicting intestinal epithelial transport of drugs in Caco-2 cell culture. J Pharm Pharm Sci 2004, 7, 186-99.
247. Ramos de Armas, R.; Gonzalez Diaz, H.; Molina, R.; Perez Gonzalez, M.; Uriarte, E., Stochastic-based descriptors studying peptides biological properties: modeling the bitter tasting threshold of dipeptides. Bioorg. Med. Chem. 2004, 12, 4815-22.
248. Gonzalez-Diaz, H.; Molina, R.; Uriarte, E., Markov entropy backbone electrostatic descriptors for predicting proteins biological activity. Bioorg. Med. Chem. Lett. 2004, 14, 4691-5.
249. Ramos de Armas, R.; Gonzalez Diaz, H.; Molina, R.; Uriarte, E., Markovian Backbone Negentropies: Molecular descriptors for protein research. I. Predicting protein stability in Arc repressor mutants. Proteins 2004, 56, 715-23.
250. Perez Gonzalez, M.; Gonzalez Diaz, H.; Molina Ruiz, R.; Cabrera, M. A.; Ramos de Armas, R., TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new herbicides. J. Chem. Inf. Comput. Sci. 2003, 43, 1192-9.
251. Gonzalez Diaz, H.; Olazabal, E.; Castanedo, N.; Sanchez, I. H.; Morales, A.; Serrano, H. S.; Gonzalez, J.; de Armas, R. R., 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, 8, 237-45.
252. Cabrera Perez, M. A.; Gonzalez Diaz, H.; Fernandez Teruel, C.; Pla-Delfina, J. M.; Bermejo Sanz, M., A novel approach to determining physicochemical and absorption properties of 6-fluoroquinolone derivatives: experimental assessment. Eur. J. Pharm. Biopharm. 2002, 53, 317-25.
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