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dc.contributor.authorQuevedo‐Tumailli, Viviana F.
dc.contributor.authorOrtega‐Tenezaca, Bernabé
dc.contributor.authorDíaz, Humberto G.
dc.date.accessioned2022-03-24T15:42:21Z
dc.date.available2022-03-24T15:42:21Z
dc.date.issued2021
dc.identifier.citationQuevedo-Tumailli, V.; Ortega-Tenezaca, B.; González-Díaz, 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, 13066. https://doi.org/10.3390/ijms222313066es_ES
dc.identifier.urihttp://hdl.handle.net/2183/30238
dc.description.abstract[Abstract] The parasite species of genus Plasmodium causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of Plasmodium sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre‐clinical assay depends on the conditions of assay per se, the chemical structure of the drug, the structure of the target protein to be targeted, as well as on factors governing the expression of this protein in the proteome such as genes (Deoxyribonucleic acid, DNA) sequence and/or chromosomes structure. However, there are no reports of computational models that consider all these factors simultaneously. Some of the difficulties for this kind of analysis are the dispersion of data in different datasets, the high heterogeneity of data, etc. In this work, we analyzed three databases ChEMBL (Chemical database of the European Molecular Biology Laboratory), UniProt (Universal Protein Resource), and NCBI‐GDV (National Center for Biotechnology Information ‐ Genome Data Viewer) to achieve this goal. The ChEMBL dataset contains outcomes for 17,758 unique assays of potential Antimalarial compounds including nu-meric descriptors (variables) for the structure of compounds as well as a huge amount of information about the conditions of assays. The NCBI‐GDV and UniProt datasets include the sequence of genes, proteins, and their functions. In addition, we also created two partitions (cassayj= cajand cdataj= cdj) of categorical variables from theChEMBL dataset. These partitions contain variables that encode information about experimental conditions of preclinical assays (caj) or about the nature and quality of data (cdj). These categorical variables include information about 22 parameters of biological activity (ca0), 28 target proteins (ca1), and 9 organisms of assay (ca2), etc. We also created another partition of (cprotj= cpj) including categorical variables with biological information about the target proteins, genes, and chromosomes. These variables cover32 genes (cp0), 10 chromosomes (cp1), gene orientation (cp2), and 31 protein functions (cp3). We used a Perturbation‐Theory Machine Learning Information Fusion (IFPTML) algorithm to map all this information (from three data-bases) into and train a predictive model. Shannon’s entropy measure Shk (numerical variables) was used to quantify the information about the structure of drugs, protein sequences, gene sequences, and chromosomes in the same information scale. Perturbation Theory Operators (PTOs) with the form of Moving Average (MA) operators have been used to quantify perturbations (deviations) in the structural variables with respect to their expected values for different subsets (partitions) of categorical variables. We obtained three IFPTML models using General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS), and Classification Tree with Linear Combinations (CTLC). The IFPTML‐CTLC presented the better performance with Sensitivity Sn(%) = 83.6/85.1, and Specificity Sp(%) = 89.8/89.7 for training/validation sets, respectively. This model could become a useful tool for the optimization of preclinical assays of new Antimalarial compounds vs. different proteins in the proteome of Plasmodium. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.es_ES
dc.description.sponsorshipH.G.‐D. personally acknowledges financial support from the Minister of Science and Innovation (PID2019‐104148GB‐I00) and a grant (IT1045‐16)—2016–2021 from the Basque Gov‐ ernment. V.Q.T. acknowledges Universidad EstatalAmazónica (UEA) scholarship for postgraduate studies; Ecuador Sciences PhD Program, (UEA.Res.26.2019.06.13)es_ES
dc.description.sponsorshipEusko Jaurlaritza = Gobierno Vasco; IT1045-16
dc.description.sponsorshipEcuador. Gobierno; UEA.Res.26.2019.06.13
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104148GB-I00/ES/NUEVAS HERRAMIENTAS SINTETICAS Y QUIMIOINFORMATICAS PARA LA CONSTRUCCION Y DIVERSIFICACION DE HETEROCICLOS ¿DRUG-LIKE¿. ACTIVACION C-H Y MACHINE LEARNING/
dc.relation.urihttps://doi.org/10.3390/ijms222313066es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAntimalarial compoundses_ES
dc.subjectChEMBLes_ES
dc.subjectComplex networkses_ES
dc.subjectMachine learninges_ES
dc.subjectNCBI‐GDVes_ES
dc.subjectPerturbation theoryes_ES
dc.subjectPlasmodium proteomees_ES
dc.subjectUniProtes_ES
dc.titleIFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compoundses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleInternational Journal of Molecular Scienceses_ES
UDC.volume22es_ES
UDC.issue23es_ES
UDC.startPage13066es_ES
dc.identifier.doi10.3390/ijms222313066


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