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Current Topics in Medicinal Chemistry(2)
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Current Computer-Aided Drug Design(1)
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SAR and QSAR in Environmental Research(1)
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scopus(6)
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:googlescopusMachine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds
ArticleAbstract: Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. HPalabras claves:antibacterial compounds, CHEMBL, complex networks, Information Fusion, Machine learning, multidrug-resistant, perturbation theoryAutores:Gerardo M. Casañola-Martin, González‐díaz H., Green J.R., Karel Diéguez-Santana, Rasulev B., Roldán Torres GutiérrezFuentes:googlescopusLearning from multiple classifier systems: Perspectives for improving decision making of QSAR models in medicinal chemistry
ReviewAbstract: Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chePalabras claves:Artificial Neural Network, Ensemble design, Histone deacetylase, Histone deacetylase (HDAC) inhibitors, Multiple classifier system, Quantitative structure –activity relationships (QSAR)Autores:Garit J., Gerardo M. Casañola-Martin, Hai D.T., Karel Diéguez-Santana, Nam N.H., Nga D.V., Pham-The H., Thu H.L.T., Yovani Marrero-PonceFuentes:googlescopusQuantitative structure–activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries
ArticleAbstract: Histone deacetylases (HDAC) are emerging as promising targets in cancer, neuronal diseases and immunPalabras claves:chemoinformatics, Histone deacetylase inhibitor, Machine learning, quantitative structure–activity relationship, Virtual ScreeningAutores:Gerardo M. Casañola-Martin, Karel Diéguez-Santana, Ngoc N.T., Nguyen-Hai N., Pham-The H., Thu H.L.T., Vu-Duc L.Fuentes:googlescopusPbkp_redicting metabolic reaction networks with Perturbation-Theory Machine Learning (PTML) models
ArticleAbstract: Background: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) modePalabras claves:Combinatorial perturbation theory models, complex networks, Linear invariants, Machine learning, Markov chains, Metabolic pathwaysAutores:Gerardo M. Casañola-Martin, González‐díaz H., Green J.R., Karel Diéguez-Santana, Rasulev B.Fuentes:googlescopusPbkp_rediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database
ArticleAbstract: In this article, the modeling of inhibitory grown activity against Tetrahymena pyriformis is describPalabras claves:CHEMBL, dragon descriptor, multiple linear regression, Phenol, QSTR, Tetrahymena pyriformisAutores:Garit J., Gerardo M. Casañola-Martin, Karel Diéguez-Santana, Pham-The H., Thu H.L.T., Villegas-Aguilar P.J.Fuentes:googlescopus