Quantitative structure–activity relationship, Linear discriminant analysis, Computational biology, Markov model and Molecular descriptor are his primary areas of study. His Quantitative structure–activity relationship research incorporates elements of Nanoparticle, Nanotechnology and Molecular model. His Computational biology research includes elements of Virtual screening, Biochemistry, Isomerase, In silico and Protein sequencing.
His work focuses on many connections between Markov model and other disciplines, such as Artificial intelligence, that overlap with his field of interest in Drug discovery. The concepts of his Molecular descriptor study are interwoven with issues in Topology, Antifungal, Biological system and Antimicrobial. His study in Stereochemistry is interdisciplinary in nature, drawing from both Biological activity and Chemometrics.
The scientist’s investigation covers issues in Quantitative structure–activity relationship, Computational biology, Markov chain, Markov model and Molecular descriptor. His Quantitative structure–activity relationship study combines topics from a wide range of disciplines, such as Biological system, Linear discriminant analysis, Molecular model and Complex network. His work deals with themes such as Proteome, Bioinformatics, Sequence alignment, chEMBL and In silico, which intersect with Computational biology.
His Markov chain study combines topics in areas such as Artificial neural network, Entropy, Statistical physics and Combinatorics. His research in Markov model intersects with topics in Drug target and Virtual screening. His research investigates the link between Molecular descriptor and topics such as Protein structure that cross with problems in Computational chemistry.
Humberto González-Díaz mainly investigates Carbon nanotube, Quantitative structure–activity relationship, Computational biology, Artificial intelligence and Machine learning. His studies deal with areas such as Rational design, Nanotoxicology, Stereochemistry, Molecular Docking Simulation and Mitochondrion as well as Carbon nanotube. The Stereochemistry study combines topics in areas such as Amino acid and Biochemistry.
In his works, Humberto González-Díaz conducts interdisciplinary research on Quantitative structure–activity relationship and Boundary value problem. The various areas that Humberto González-Díaz examines in his Computational biology study include Gene, Bioinformatics, Cheminformatics and chEMBL. His study in Linear discriminant analysis extends to Machine learning with its themes.
His main research concerns Carbon nanotube, Artificial intelligence, Machine learning, Neuropharmacology and Interactome. His Carbon nanotube study integrates concerns from other disciplines, such as Stereochemistry, Nanotoxicology and Molecular Docking Simulation. His Artificial intelligence research incorporates themes from Yield, Electrophile, MEDLINE, Computational learning theory and Big data.
Many of his research projects under Machine learning are closely connected to Substitution and Perturbation theory with Substitution and Perturbation theory, tying the diverse disciplines of science together. His Neuropharmacology research is multidisciplinary, incorporating perspectives in Bioinformatics, Cheminformatics, Drug target, chEMBL and Complex network. He has included themes like Artificial neural network, Quantitative structure–activity relationship and Computational biology in his Complex network study.
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Medicinal chemistry and bioinformatics--current trends in drugs discovery with networks topological indices.
Humberto Gonzalez-Diaz;Santiago Vilar;Lourdes Santana;Eugenio Uriarte.
Current Topics in Medicinal Chemistry (2007)
Proteomics, networks and connectivity indices.
Humberto González-Díaz;Yenny González-Díaz;Lourdes Santana;Florencio M. Ubeira.
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.
Francisco J. Prado-Prado;Humberto González-Díaz;Octavio Martinez de la Vega;Florencio M. Ubeira.
Bioorganic & Medicinal Chemistry (2008)
Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach.
Humberto Gonzalez-Diaz;Francisco Prado-Prado;Florencio M. Ubeira.
Current Topics in Medicinal Chemistry (2008)
A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins.
Lourdes Santana;Eugenio Uriarte;Humberto González-Díaz;Giuseppe Zagotto.
Journal of Medicinal Chemistry (2006)
Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species
Francisco J. Prado-Prado;Xerardo García-Mera;Humberto González-Díaz.
Bioorganic & Medicinal Chemistry (2010)
Quantitative structure-activity relationship and complex network approach to monoamine oxidase A and B inhibitors.
Lourdes Santana;Humberto González-Díaz;Elías Quezada;Eugenio Uriarte.
Journal of Medicinal Chemistry (2008)
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.
Francisco J. Prado-Prado;Francisco J. Prado-Prado;Octavio Martinez de la Vega;Eugenio Uriarte;Florencio M. Ubeira.
Bioorganic & Medicinal Chemistry (2009)
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.
Valeria V. Kleandrova;Feng Luan;Humberto González-Díaz;Juan M. Ruso.
Environmental Science & Technology (2014)
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.
Guillermín Agüero-Chapin;Humberto González-Díaz;Humberto González-Díaz;Reinaldo Molina;Reinaldo Molina;Javier Varona-Santos.
FEBS Letters (2006)
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