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Chemical Biology and Drug Design(1)
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Beyond 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:scopusA 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:googlescopusQSPR/QSAR analyses by means of the CORAL software: Results, challenges, perspectives
Book PartAbstract: In this chapter, the methodology of building up quantitative structure-property/activity relationshiPalabras claves:Autores:Bacelo D.E., Benfenati E., Carotti A., Castro E.A., Leszczynska D., Leszczynski J., Nesmerak K., Nicolotti O., Pablo R. Duchowicz, Rasulev B., Toropov A.A., Toropova A.P., Veselinović A.M., Veselinović J.B.Fuentes:scopus