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Humbert Gonzalez Diaz     Senior Scientist or Principal Investigator 
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Humbert Gonzalez Diaz published an article in May 2016.
Top co-authors See all
Feng Luan

66 shared publications

Sonia Arrasate

24 shared publications

Esther Lete

21 shared publications

Yovani Marrero-Ponce

11 shared publications

Publication Record
Distribution of Articles published per year 
(2013 - 2016)
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Article 1 Read 0 Citations Computational modeling and experimental facts of mixed self-assembly systems. Paula V Messina, Jose Miguel Besada-Porto, Ramón Rial, Humbe... Published: 13 May 2016
Curr. Pharm. Des.,
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The formation of liposomes, nanoparticle micelles, and related systems by mixtures of drugs and/or surfactants is of major relevance for the design of drug delivery systems. We can design new systems using different compounds. Traditionally these systems are created by trial and error using experimental data. However, in most cases measure all the possible combinations represents a extensive work and almost always unaffordable. In this sense, we can use theoretical concepts and develop computational models to predict different physicochemical properties of self-aggregation processes of mixed molecular systems. In a previous work, we developed a new PT-LFER model (Linear Free Energy Relationships, LFER, combined with Perturbation Theory, PT, ideas) for binary systems. The best PT-LFER model found predicted the effects of 25000 perturbations over nine different properties of binary systems. The present work have two parts. First, we carry out an analysis on the new results on the applications and experimental-theoretical studies of binary self-assembled systems. In the second part we report, by the first time, a new experimental-theoretic study of the NaDC-DTAB binary system. For this purpose, we have combined experimental procedures plus physicochemical thermodynamic framework with the PT-LFER model reported in our previous work.
Article 1 Read 0 Citations Multi-output Model with Box-Jenkins Operators of Quadratic Indices for Prediction of Malaria and Cancer Inhibitors Targe... Huong Le-Thi-Thu, Facundo Pérez-Giménez, Yovani Marrero-Ponc... Published: 01 January 2016
Curr. Protein Pept. Sci.,
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The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory proteins. Cellular processes such as the cell cycle, signal transduction, gene expression, DNA repair and apoptosis are regulated by this UPP and dysfunctions in this system have important implications in the development of cancer, neurodegenerative, cardiac and other human pathologies. UPP seems also to be very important in the function of eukaryote cells of the human parasites like Plasmodium falciparum, the causal agent of the neglected disease Malaria. Hence, the UPP could be considered as an attractive target for the development of compounds with Anti-Malarial or Anti-cancer properties. Recent online databases like ChEMBL contains a larger quantity of information in terms of pharmacological assay protocols and compounds tested as UPP inhibitors under many different conditions. This large amount of data give new openings for the computer-aided identification of UPP inhibitors, but the intrinsic data diversity is an obstacle for the development of successful classifiers. To solve this problem here we used the Bob-Jenkins moving average operators and the atom-based quadratic molecular indices calculated with the software TOMOCOMD-CARDD (TC) to develop a quantitative model for the prediction of the multiple outputs in this complex dataset. Our multi-target model can predict results for drugs against 22 molecular or cellular targets of different organisms with accuracies above 70% in both training and validation sets.
Article 1 Read 0 Citations Editorial: Chemoinformatics in metabolomics, modeling chemical reactivity and ADMET processes part 1. Humberto Gonzalez-Diaz, Alejandro Speck-Planche, Maria Natal... Published: 01 January 2014
Curr. Drug Metab.,
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Article 1 Read 0 Citations Chemoinformatics in metabolomics, from molecular mechanics, dynamics, and docking to complex metabolic networks, part 2. Humberto Gonzalez-Diaz, Alejandro Speck-Planche, Maria Natal... Published: 01 January 2014
Curr. Drug Metab.,
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Article 1 Read 0 Citations QSPR and flow cytometry analysis (QSPR-FCA): review and new findings on parallel study of multiple interactions of chemi... Esvieta Tenorio-Borroto, Fabiola Rivera Ramirez, Alejandro S... Published: 01 January 2014
Curr. Drug Metab.,
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The immune system helps to halt the infections caused by pathogenic microbial and parasitic agents. The ChEMBL database lists very large datasets of cytotoxicity of organic compounds but notably, a large number of compounds have unknown effects over molecular and cellular targets in the immune system. Flow Cytometry Analysis (FCA) is a very important technique to determine the effect of organic compounds over these molecular and cellular targets in the immune system. In addition, multi-target Quantitative Structure- Property Relationship (mt-QSPR) models can predict drug-target interactions, networks. The objectives of this paper are the following. Firstly, we carried out a review of general aspects and some examples of applications of FCA to study the effect of drugs over different cellular targets. However, we focused more on methods, materials, and experimental results obtained in previous works reported by our group in the study of the drug Dermofural. We also reviewed different mt-QSPR models useful to predict the immunotoxicity and/or the effects of drugs over immune system targets including immune cell lineages or proteins. Secondly, we included new results not published before. Initially, we used ChEMBL data to train and validate a new model but with emphasis in the effect of drugs over lymphocytes. Lastly, we report unpublished results of the computational and FCA study of a new nitro-vinyl-furan compound over thymic lymphocytes T helpers (CD4+) and T cytotoxic (CD8+) population.
Article 2 Reads 0 Citations Matrix trace operators: from spectral moments of molecular graphs and complex networks to perturbations in synthetic rea... Humberto Gonzalez-Diaz, Sonia Arrasate, Asier Gomez-San Juan... Published: 01 January 2014
Curr. Drug Metab.,
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The study of quantitative structure-property relationships (QSPR) is important to study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the effects of in-out perturbations in intra-molecular carbolithiations, drug ADME process, or self-aggregation of micelle nanoparticles of drugs or surfactants. The Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp) of these models were >90% in all cases. The first model predicts variations in the yield or enantiomeric excess due to structural variations or changes in the solvent, temperature, temperature of addition, or time of reaction. The second model predicts changes in >18 parameters of biological effects for >3000 assays of ADME properties and/or interactions between 31,723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model predicts perturbations due to changes in temperature, solvent, salt concentration, and/or structure of anions or cations in the self-aggregation of micelle nanoparticles of drugs and surfactants.