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Atom based linear index descriptors in QSAR-machine learning classifiers for the pbkp_rediction of ubiquitin-proteasome pathway activity
ArticleAbstract: Abstract: This report showed the use of the atom-based linear index together with different classicPalabras claves:Atom-based linear indices, Machine learning, QSAR, ToMoCoMD-CARDD software, Ubiquitin-proteasome pathway inhibitorsAutores:Garit J., Gerardo M. Casañola-Martin, Pham-The H., Thu H.L.T.Fuentes:scopusAnalysis of proteasome inhibition pbkp_rediction using atom-based quadratic indices enhanced by machine learning classification techniques
ArticleAbstract: In this work the use of 2D atom-based quadratic indices is shown in the pbkp_rediction of proteasomePalabras claves:Atom-based quadratic index, Classification and regression model, Machine learning, Proteasome inhibition, QSAR, ToMoCoMD-CARDD softwareAutores:Abad C., Garit J., Gerardo M. Casañola-Martin, Pérez-Giménez F., Thu H.L.T., Torrens F., Yovani Marrero-PonceFuentes:scopusA 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:scopusIn 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: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:googlescopusThe machine learning techniques in the protein structure pbkp_rediction: An approach from bioinformatics
ArticleAbstract: The pbkp_rediction of protein structures remains as a challenge for the scientific community. For thPalabras claves:Artificial Intelligence, bioinformatics, Machine learning, Protein structure pbkp_redictionAutores:Aguilar-Ruiz J.S., Gerardo M. Casañola-Martin, Santiesteban-Toca C.E.Fuentes:scopus