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A 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 pbkp_rediction of α-amylase andPalabras 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:scopusMachine 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:googlescopusMachine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends
ArticleAbstract: Machine learning (ML) methods are used in cheminformatics processes to pbkp_redict the activity of aPalabras claves:Antibacterial agents, Antibiotic Resistance, Bibliometric Analysis, Computer model in drug design, Machine learning, Network AnalysisAutores:González‐díaz H., Karel Diéguez-SantanaFuentes: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 pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches
ArticleAbstract: Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesPalabras claves:Aquatic toxicity, Machine learning, multiple linear regression, Quantitative Structure–Toxicity relationship, random forestAutores:Amilkar Puris, González‐díaz H., Karel Diéguez-Santana, Nachimba-Mayanchi M.M., Roldán Torres GutiérrezFuentes:googlescopus