Karel Diéguez-Santana

Bio: Karel Diéguez-Santana is an academic researcher from University of the Basque Country. The author has contributed to research in topics: Medicine & Cheminformatics. The author has an hindex of 5, co-authored 18 publications receiving 73 citations.

Quantitative structure–activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries

Abstract: Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immune disorders. Computational modelling approaches have been widely applied for the virtual screening and rational design of novel HDAC inhibitors. In this study, different machine learning (ML) techniques were applied for the development of models that accurately discriminate HDAC2 inhibitors form non-inhibitors. The obtained models showed encouraging results, with the global accuracy in the external set ranging from 0.83 to 0.90. Various aspects related to the comparison of modelling techniques, applicability domain and descriptor interpretations were discussed. Finally, consensus predictions of these models were used for screening HDAC2 inhibitors from four chemical libraries whose bioactivities against HDAC1, HDAC3, HDAC6 and HDAC8 have been known. According to the results of virtual screening assays, structures of some hits with pair-isoform-selective activity (between HDAC2 and other HDACs) were revealed. This study illustrates the power of ML-based QSAR approaches for the screening and discovery of potent, isoform-selective HDACIs.

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models

Abstract: Background Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology. Objective In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions). Results The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series. Methods In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs g40 organisms compiled by Barabasis' group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms. Conclusion Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.

Plots Transformations And Regression An Introduction To Graphical Methods Of Diagnostic Regression Analysis

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Construction of noninterpenetrating and interpenetrating Co(ii) networks with halogenated carboxylate modulated by auxiliary N-donor co-ligands: structural diversity, electrochemical and photocatalytic properties

Abstract: Six Co(II)-based coordination polymers (CPs) with characteristic frameworks and topologies—namely, [Co(L1)(DCTP)]n (1), [Co(L2)(DCTP)]n (2), [Co(L3)(DCTP)]n (3), {[Co3(L4)3(DCTP)3·H2O]·H2O}n (4), [Co(L5)1.5(DCTP)]n (5) and [Co(L6)(DCTP)]n (6)—were successfully hydrothermally synthesized by employing the halogenated linear ligand 2,5-dichloroterephthalic acid (H2DCTP). The interpenetrated structures could be rationally modulated by auxiliary N-donor co-ligands containing 1,1′-(1,4-butanediyl)bis-1H-benzimidazole (L1), 1,4-bis(5,6-dimethylbenzimidazol-1-yl)-2-butylene (L2), 1,2-bis(2-methylbenzimidazol-1-ylmethyl)benzene (L3), 1,4-bis(2-methylbenzimidazol-1-ylmethyl)benzene (L4), 1,2-bis(5,6-dimethylbenzimidazol-1-ylmethyl)benzene (L5) and 1,4-bis(5,6-dimethylbenzimidazol-1-ylmethyl)benzene (L6). These diaphanous crystals were clearly characterized by elemental analysis, infrared (IR) spectra and X-ray powder diffraction (XRPD) as well as single-crystal X-ray diffraction analysis. With the aid of the flexible N-donor co-ligands, CP 1 occupies a non-interpenetrated 2D sheet with the point symbol {44·62} sql net topology, CP 2 possesses a 3D hexagon-shaped network with the point symbol {66} three-fold interpenetrated sqc6 topology, CP 3 exhibits a 2D layer with the point symbol {44·62} sql net topology, CP 4 reveals an unusual 3D framework with the point symbol {42·63·8} three-fold interpenetrated sra topology, CP 5 has a 3D hexagon-shaped network with the point symbol {66} two-fold interpenetrated sqc6 topology, while CP 6 displays a 3D hexagon-shaped network with the point symbol {66} three-fold interpenetrated sqc6 topology. The diverse structures of CPs 1–6 illustrate that the substitute group and position of the methyl group of the bis(benzimidazole) derivatives play a significant role in the assembly of such interpenetrated frameworks. Moreover, luminescence properties and thermal behavior, as well as the electrochemical and photocatalytic properties of CPs 1–6 on the degradation of methylene blue, are also presented.