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Exploring the QSAR’s predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets
ArticleAbstract: Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneouPalabras claves:CARDD suite, DIVATI module, feature selection, free and open source software, genetic algorithm, Friedman test for multiple comparisons, graph derivative indices, Keysfinder framework, Molecular descriptors, multiple linear regression, QSAR model, TOMOCOMD systemAutores:Hurtado E., Orozco-Ugarriza M.E., Oscar Martínez Santiago, Ramos Y., Rivera-Borroto O.M., Treto-Suarez M.A., Vergara-Murillo F., Vivas-Reyes R., Yoan Martínez López, Yovani Marrero-PonceFuentes:scopusBond-based linear indices in QSAR: Computational discovery of novel anti-trichomonal compounds
ArticleAbstract: Trichomonas vaginalis (Tv) is the causative agent of the most common, non-viral, sexually transmittePalabras claves:Bond-based linear indices, In vitro cytostatic and cytocidal activities, LDA-assisted QSAR model, ToMoCoMD-CARDD software, Trichomonacidal, Virtual ScreeningAutores:Arán V.J., García-Domenech R., García-Trevijano J.A.E., Gómez-Barrio A., Grau-Ábalo R.d.C., Julián-Ortiz J.V., Meneses-Marcel A., Montero A., Nogal-Ruiz J.J., Pereira D.M., Rivera-Borroto O.M., Torrens F., Vogel C., Yovani Marrero-PonceFuentes:googlescopusA Fuzzy System Classification Approach for QSAR Modeling of αAmylase and α-Glucosidase Inhibitors
ArticleAbstract: Introduction: This report proposes the application of a new Machine Learning algorithm called FuzzyPalabras claves:Anti-diabetic agents, FURIA-C, induction rule, Lda, machine-learning techniques, QSARAutores:Amilkar Puris, Gerardo M. Casañola-Martin, González‐díaz H., Karel Diéguez-Santana, Rasulev B., Rivera-Borroto O.M.Fuentes:googlescopusA two QSAR way for antidiabetic agents targeting using α-amylase and α-glucosidase inhibitors: Model parameters settings in artificial intelligence techniques
ArticleAbstract: This work showed the use of 0-2D Dragon molecular descriptors in the prediction of α-amylase and α-gPalabras claves:classification model, dragon descriptor, Machine learning, QSAR., α-amylase, α-GlucosidaseAutores:Amilkar Puris, Gerardo M. Casañola-Martin, Hai P.T., Karel Diéguez-Santana, Rivera-Borroto O.M., Thu H.L.T.Fuentes:scopusComparison of combinatorial clustering methods on pharmacological data sets represented by machine learning-selected real molecular descriptors
ArticleAbstract: Cluster algorithms play an important role in diversity related tasks of modern chemoinformatics, witPalabras claves:Autores:García-De La Vega J.M., Grau-Ábalo R.d.C., Rivera-Borroto O.M., Yovani Marrero-PonceFuentes:googlescopusComparison of novelproximity models in Chemoinformatics
ArticleAbstract: This work comprises the computational implementation in the Java environment of 21 proximity modelsPalabras claves:Machine-learning descriptor selection, Medicinal chemistry data set, Proximity model, Similarity searchingAutores:Del Corazón Grau Ábalo R., Díaz Y.H., García-De La Vega J.M., Rivera-Borroto O.M., Yovani Marrero-PonceFuentes:scopusBeyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies
ArticleAbstract: In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and αPalabras claves:antidiabetic agents, Decision Trees, linear discriminant analysis, QSARAutores:Amilkar Puris, Gerardo M. Casañola-Martin, Karel Diéguez-Santana, Pham-The H., Rasulev B., Rivera-Borroto O.M., Thu H.L.T.Fuentes:scopusDunn's index for cluster tendency assessment of pharmacological data sets
ArticleAbstract: Cluster tendency assessment is an important stage in cluster analysis. In this sense, a group of proPalabras claves:Classification accuracy, CLÚSTER ANALYSIS, Cluster tendency, Clusters overlap, Dunn's index, Pharmacological data sets, VAT techniquesAutores:García-De La Vega J.M., Grau-Ábalo R.d.C., Rabassa-Gutiérrez M., Rivera-Borroto O.M., Yovani Marrero-PonceFuentes:googlescopusOptimum search strategies or novel 3D molecular descriptors: Is there a stalemate?
ArticleAbstract: The present manuscript describes a novel 3D-QSAR alignment free method (QuBiLS-MIDAS Duplex) based oPalabras claves:3D-QSAR, aggregation operator, Alignment free method, Minkowski distance matrix, Principal Component Analysis, QuBiLS-MIDAS, ToMoCoMD-CARDD, Two-tuple spatial-(dis)similarity matrix, Variability analysisAutores:Alvarado Y.J., César R. García-Jacas, Cubillan N., Pino-Urias R.W., Rivera-Borroto O.M., Stephen Jones Barigye, Thu H.L.T., Valdés-Martiní J.R., Yovani Marrero-PonceFuentes:scopusOverview on the drug development process and molecular similarity-based virtual screening techniques
ReviewAbstract: Drug development is a very complex task but also very appreciated by the sensibility that generatesPalabras claves:Drug development process, Molecular similarity, Similarity searching, Virtual screening techniquesAutores:García-De La Vega J.M., Grau-Ábalo R.d.C., Hernández Díaz Y., Maykel Cruz-Monteagudo, Rivera-Borroto O.M., Yovani Marrero-PonceFuentes:scopus