Michael Fernandez Llamosa
Michael Fernandez Llamosa
Machine Learning Specialist
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The value of antimicrobial peptides in the age of resistance
M Magana, M Pushpanathan, AL Santos, L Leanse, M Fernandez, ...
The Lancet Infectious Diseases 20 (9), e216-e230, 2020
Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture
M Fernandez, PG Boyd, TD Daff, MZ Aghaji, TK Woo
The journal of physical chemistry letters 5 (17), 3056-3060, 2014
Large-scale quantitative structure–property relationship (QSPR) analysis of methane storage in metal–organic frameworks
M Fernandez, TK Woo, CE Wilmer, RQ Snurr
The Journal of Physical Chemistry C 117 (15), 7681-7689, 2013
Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines
M Fernandez, D Miranda-Saavedra
Nucleic acids research 40 (10), e77-e77, 2012
Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)
M Fernandez, J Caballero, L Fernandez, A Sarai
Molecular diversity 15, 269-289, 2011
Quantitative structure–activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds
M Fernández, J Caballero, AM Helguera, EA Castro, MP González
Bioorganic & medicinal chemistry 13 (9), 3269-3277, 2005
Atomic property weighted radial distribution functions descriptors of metal–organic frameworks for the prediction of gas uptake capacity
M Fernandez, NR Trefiak, TK Woo
The Journal of Physical Chemistry C 117 (27), 14095-14105, 2013
QSAR for non-nucleoside inhibitors of HIV-1 reverse transcriptase
PR Duchowicz, M Fernández, J Caballero, EA Castro, FM Fernández
Bioorganic & medicinal chemistry 14 (17), 5876-5889, 2006
Artificial neural networks from MATLAB® in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): Application to the prediction of the antagonistic activity …
J Caballero, M Fernández
Current topics in medicinal chemistry 8 (18), 1580-1605, 2008
Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks
J Caballero, M Fernández
Journal of Molecular Modeling 12, 168-181, 2006
Toxic colors: the use of deep learning for predicting toxicity of compounds merely from their graphic images
M Fernandez, F Ban, G Woo, M Hsing, T Yamazaki, E LeBlanc, ...
Journal of chemical information and modeling 58 (8), 1533-1543, 2018
Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl) amino] acetamide derivatives as matrix metalloproteinase inhibitors
M Fernández, J Caballero, A Tundidor-Camba
Bioorganic & medicinal chemistry 14 (12), 4137-4150, 2006
Modeling of activity of cyclic urea HIV-1 protease inhibitors using regularized-artificial neural networks
M Fernández, J Caballero
Bioorganic & medicinal chemistry 14 (1), 280-294, 2006
Geometrical Properties Can Predict CO2 and N2 Adsorption Performance of Metal–Organic Frameworks (MOFs) at Low Pressure
M Fernandez, AS Barnard
acs combinatorial science 18 (5), 243-252, 2016
Amino Acid Sequence Autocorrelation vectors and ensembles of Bayesian-Regularized Genetic Neural Networks for prediction of conformational stability of human lysozyme mutants
J Caballero, L Fernandez, JI Abreu, M Fernández
Journal of chemical information and modeling 46 (3), 1255-1268, 2006
Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support …
J Caballero, L Fernández, M Garriga, JI Abreu, S Collina, M Fernández
Journal of Molecular Graphics and Modelling 26 (1), 166-178, 2007
Transglutaminase‐catalyzed synthesis of trypsin–cyclodextrin conjugates: Kinetics and stability properties
R Villalonga, M Fernandez, A Fragoso, R Cao, P Di Pierro, L Mariniello, ...
Biotechnology and bioengineering 81 (6), 732-737, 2003
Modeling of cyclin-dependent kinase inhibition by 1 H-pyrazolo [3, 4-d] pyrimidine derivatives using artificial neural network ensembles
M Fernandez, A Tundidor-Camba, J Caballero
Journal of chemical information and modeling 45 (6), 1884-1895, 2005
Quantitative structure–property relationship models for recognizing metal organic frameworks (MOFs) with high CO2 working capacity and CO2/CH4 selectivity for methane purification
MZ Aghaji, M Fernandez, PG Boyd, TD Daff, TK Woo
European Journal of Inorganic Chemistry 2016 (27), 4505-4511, 2016
Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches
MP González, J Caballero, A Tundidor-Camba, AM Helguera, ...
Bioorganic & medicinal chemistry 14 (1), 200-213, 2006
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