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In Silico Pbkp_rediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach
ArticleAbstract: In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative StrPalabras claves:Accumulated Local Effect, ensemble model, Machine learning, nitroaromatic compounds, QSAR, QSTR, Support Vector Machine, ToxicityAutores:Daghighi A., Gerardo M. Casañola-Martin, Lučić B., Milenković D., Rasulev B., Timmerman 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: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