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Machine 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., Humbert González-Díaz, Karel Diéguez-Santana, Rasulev B., Roldán Torres GutiérrezFuentes:googlescopusIn Silico Prediction 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:scopusFirst report on Quantitative Structure-Toxicity Relationship modeling approaches for the prediction of acute toxicity of various organic chemicals against rotifer species
ArticleAbstract: Nowadays, organic chemicals are crucial components in virtually every aspect of daily life, servingPalabras claves:ecotoxicity, Machine Learning, multiple linear regression, quantitative structure-toxicity relationship, Rotifer, Support Vector RegressionAutores:Gerardo M. Casañola-Martin, González‐díaz H., Karel Diéguez-Santana, Rasulev B., Roldán Torres GutiérrezFuentes:scopusPredicting 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:googlescopusMachine learning based assessment of the toxicity of polymeric materials to green algae as an approach to design non-toxic polymeric coatings to deter marine biofouling
ArticleAbstract: Polymeric coatings are extremely important and widely applied materials. However, the widespread usaPalabras claves:Green algae, Machine Learning, Polymeric coatings, predictive model, QSAR, toxicityAutores:Gerardo M. Casañola-Martin, Joan Manuel Rodriguez-Diaz, Rasulev B., Seyedehelham ShirvanihosseiniFuentes:scopus