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Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies
ArticleAbstract: In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and αPalabras claves:antidiabetic agents, Decision Trees, linear discriminant analysis, QSARAutores:Amilkar Puris, Gerardo M. Casañola-Martin, Karel Diéguez-Santana, Pham-The H., Rasulev B., Rivera-Borroto O.M., Thu H.L.T.Fuentes:scopusA Fuzzy System Classification Approach for QSAR Modeling of αAmylase and α-Glucosidase Inhibitors
ArticleAbstract: Introduction: This report proposes the application of a new Machine Learning algorithm called FuzzyPalabras claves:Anti-diabetic agents, FURIA-C, induction rule, Lda, machine-learning techniques, QSARAutores:Amilkar Puris, Gerardo M. Casañola-Martin, González‐díaz H., Karel Diéguez-Santana, Rasulev B., Rivera-Borroto O.M.Fuentes:googlescopusDevelopment of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer’s Disease
ArticleAbstract: Multi-target drug development has become an attractive strategy in the discovery of drugs to treat oPalabras claves:AChE, Alzheimer’s disease, BACE1, dual-target inhibitor, fragment design, QSARAutores:Baecker D., Bao L.Q., Gerardo M. Casañola-Martin, Huong T.T.L., Mai Dung D.T., Nam N.H., Nguyen P.L., Pham-The H., Phuong Dung P.T., Phuong Nhung N., Rasulev B., Thi Thuan N.Fuentes:scopusCarbon nanotubes’ effect on mitochondrial oxygen flux dynamics: Polarography experimental study and machine learning models using star graph trace invariants of raman spectra
ArticleAbstract: This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) undPalabras claves:carbon nanotubes, Cytotoxicity, Graph Theory, Mitochondria oxygen mass flux, Raman spectroscopy, Spectral momentsAutores:Barreiro Sorrivas J.M., Gerardo M. Casañola-Martin, González-Durruthy M., González‐díaz H., Maojo V., Monserrat J.M., Munteanu C.R., Paraíso-Medina S., Rasulev B., Sierra A.P.Fuentes:scopusExploring proteasome inhibition using atomic weighted vector indices and machine learning approaches
ArticleAbstract: Ubiquitin–proteasome system (UPS) is a highly regulated mechanism of intracellular protein degradatiPalabras claves:AWV, deep learning, Descriptor, GA, ML, UpsAutores:Garit J., Gerardo M. Casañola-Martin, Oscar Martínez Santiago, Rasulev B., Rodríguez-González A.Y., Stephen Jones Barigye, Yoan Martínez LópezFuentes: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:scopusIn silico assessment of ADME properties: Advances in Caco-2 cell monolayer permeability modeling
ReviewAbstract: One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substanPalabras claves:Adme, Biopharmaceutics classification system (BCS), Caco-2 cell permeability, Human intestinal absorption, In vitro-in vivo correlation (IVIVC), QSAR/QSPRAutores:Garit J., Gerardo M. Casañola-Martin, Nam N.H., Pérez M.A.C., Pham-The H., Rasulev B., 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:googlescopusTowards rational nanomaterial design by pbkp_redicting drug-nanoparticle system interaction vs. bacterial metabolic networks
ArticleAbstract: The emergence of multidrug-resistant (MDR) strains with perturbed metabolic networks (MNs) pushes rePalabras claves:Autores:González‐díaz H., Karel Diéguez-Santana, Rasulev B.Fuentes:googlescopusQSPR/QSAR analyses by means of the CORAL software: Results, challenges, perspectives
Book PartAbstract: In this chapter, the methodology of building up quantitative structure-property/activity relationshiPalabras claves:Autores:Bacelo D.E., Benfenati E., Carotti A., Castro E.A., Leszczynska D., Leszczynski J., Nesmerak K., Nicolotti O., Pablo R. Duchowicz, Rasulev B., Toropov A.A., Toropova A.P., Veselinović A.M., Veselinović J.B.Fuentes:scopus