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A 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: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:scopus