<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2074-8647</journal-id>
<journal-title><![CDATA[Biotecnología Vegetal]]></journal-title>
<abbrev-journal-title><![CDATA[Biot. Veg.]]></abbrev-journal-title>
<issn>2074-8647</issn>
<publisher>
<publisher-name><![CDATA[Instituto de Biotecnología de las PlantasUniversidad Central Marta Abreu de Las Villas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2074-86472019000200113</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Predicción de la afinidad de ligandos antagonistas por receptores de adenosina A2A usando árboles de decisión]]></article-title>
<article-title xml:lang="en"><![CDATA[Predicting the affinity of A2A adenosine receptor antagonists using decision trees]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Huici Corrales]]></surname>
<given-names><![CDATA[Adonis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morales Helguera]]></surname>
<given-names><![CDATA[Aliuska]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ancede Gallardo]]></surname>
<given-names><![CDATA[Evys]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Molina Ruiz]]></surname>
<given-names><![CDATA[Reinaldo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Teijeira Bautista]]></surname>
<given-names><![CDATA[Marta]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Abreu Stincer]]></surname>
<given-names><![CDATA[Guillermo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez Albelo]]></surname>
<given-names><![CDATA[Eugenio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Central Marta Abreu de Las Villas Centro de Bioactivos Químicos ]]></institution>
<addr-line><![CDATA[Santa Clara Villa Clara]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Andrés Bello Centro de Nanociencias Aplicadas (CENAP) ]]></institution>
<addr-line><![CDATA[Santiago Santiago]]></addr-line>
<country>Chile</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Santiago de Compostela Departamento de Química Orga&#769;nica, Facultad de Farmacia ]]></institution>
<addr-line><![CDATA[Santiago de Compostela La Corun&#771;a]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Central Marta Abreu de Las Villas Departamento de Química ]]></institution>
<addr-line><![CDATA[Santa Clara Villa Clara]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<volume>19</volume>
<numero>2</numero>
<fpage>113</fpage>
<lpage>126</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2074-86472019000200113&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2074-86472019000200113&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2074-86472019000200113&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Las enfermedades neurodegenerativas están siendo tratadas mediante la modulación de los receptores de adenosina con antagonistas más eficaces, seguros y selectivos. El objetivo del estudio consistió en desarrollar una metodología para obtener modelos de clasificación sobre la base de algoritmos de árboles de decisión y descriptores de 0D a 2D de familias no congenéricas de compuestos orgánicos para predecir cualitativamente la afinidad ligando-RAA2A. Para ello se construyó y curó una base de datos no congenérica de 315 antagonistas con su constante de inhibición en nano molar, etiquetados como potentes y débiles. Se utilizaron los programas Dragon e ISIDA/QSPR para calcular descriptores moleculares y se obtuvieron cinco grupos de descriptores. En cada grupo se seleccionaron 50 descriptores usando el criterio mRMR. La base de datos se dividió en series de Entrenamiento, Prueba y Externa mediante una selección aleatoria y un análisis de clúster k-means generalizado. Se desarrollaron y validaron clasificadores utilizando el programa WEKA. Los resultados fueron analizados mediante las pruebas estadísticas de Friedman y Wilcoxon. Se comprobó la influencia significativa del parámetro m del algoritmo J48 en la predictividad, para los modelos que usaron los descriptores de los grupos aug.a-b e hyb.aug.a del ISIDA/QSPR. El modelo de mejor desempeño se obtuvo de los descriptores seleccionados del grupo ISIDA-todos con un valor de m=6 y alcanzó 90.6% de predicción sobre la serie Externa. La metodología desarrollada para obtener modelos de clasificación sobre la base de algoritmos de árboles de decisión y descriptores de 0D a 2D de familias no congenéricas de compuestos orgánicos es efectiva para predecir cualitativamente la afinidad ligando-RAA2Acon una exactitud, especificidad y selectividad superiores al 90%.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Neurodegenerative diseases are being treated by modulating adenosine receptors with more effective, safe and selective antagonists. The objective of the study was to develop a methodology to obtain classification models based on decision tree algorithms and descriptors from 0D to 2D of non-congenital families of organic compounds to qualitatively predict ligand-RAA2A affinity. For this purpose, a non-congeneric database of 315 antagonists was constructed and cured with its inhibition constant in nano molar, labeled as potent and weak. The Dragon and ISIDA / QSPR programs were used to calculate molecular descriptors and five groups of descriptors were obtained. In each group 50 descriptors were selected using the mRMR criterion. The database was divided into Training, Test and External series through a random selection and a generalized k-means cluster analysis. Classifiers were developed and validated using the WEKA program. The results were analyzed using the statistical tests of Friedman and Wilcoxon. The significant influence of parameter m of algorithm J48 on the predictivity was verified for the models that used the descriptors of the aug.a-b and hyb.aug.a groups of ISIDA / QSPR. The best performance model was obtained from the selected descriptors of the ISIDA-all group with a value of m = 6 and reached 90.6% prediction on the External series. The methodology developed to obtain classification models based on decision tree algorithms and descriptors from 0D to 2D of non-congenital families of organic compounds is effective in qualitatively predicting ligand-RAA2A affinity with accuracy, specificity and selectivity greater than 90 %.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[aprendizaje automatizado]]></kwd>
<kwd lng="es"><![CDATA[clasificación]]></kwd>
<kwd lng="es"><![CDATA[modelación]]></kwd>
<kwd lng="es"><![CDATA[QSAR]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[modeling]]></kwd>
<kwd lng="en"><![CDATA[QSAR]]></kwd>
</kwd-group>
</article-meta>
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