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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 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:googlescopusSimulation strategy to reduce quality uncertainty in the sugar cane honey process design
ArticleAbstract: This work proposes to increase the acceptability of the sensory quality attributes of sugarcane honePalabras claves:Sigma quality level, Simulation, Sugarcane honey, UNCERTAINTYAutores:Cortes I.B., Galo Cerda-Mejia, Galo Leonardo Cerda Mejía, González-Suaréz E., Guardado-Yordi E., Karel Diéguez-Santana, Martínez A.P., Victor Cerda-MejíaFuentes: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:googlescopus