Mostrando 3 resultados de: 3
Filtros aplicados
Publisher
Current Computer-Aided Drug Design(1)
Quantitative Structure-Activity Relationships in Drug Design, Pbkp_redictive Toxicology, and Risk Assessment(1)
Toxics(1)
A 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:googlescopusIn 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:scopusQSPR/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