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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: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:scopusNovel coumarin-based tyrosinase inhibitors discovered by OECD principles-validated QSAR approach from an enlarged, balanced database
ArticleAbstract: The present work is devoted to the development and application of a multi-agent Quantitative StructuPalabras claves:Atom-based bilinear index, coumarin, In silico identification, In vitro corroboration, linear discriminant analysis, OECD principle, QSAR model, ToMoCoMD-CARDD software, Tyrosinase inhibitorAutores:Abad C., Gerardo M. Casañola-Martin, Parmar V.S., Rescigno A., Saso L., Thu H.L.T., Torrens F., Yovani Marrero-PonceFuentes:scopusLearning from multiple classifier systems: Perspectives for improving decision making of QSAR models in medicinal chemistry
ReviewAbstract: Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chePalabras claves:Artificial Neural Network, Ensemble design, Histone deacetylase, Histone deacetylase (HDAC) inhibitors, Multiple classifier system, Quantitative structure –activity relationships (QSAR)Autores:Garit J., Gerardo M. Casañola-Martin, Hai D.T., Karel Diéguez-Santana, Nam N.H., Nga D.V., Pham-The H., Thu H.L.T., Yovani Marrero-PonceFuentes:googlescopusTowards better BBB passage pbkp_rediction using an extensive and curated data set
ArticleAbstract: In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) isPalabras claves:BBB endpoint, Blood£brain barrier, dragon descriptor, linear discriminant analysis, multiple linear regression, P-Glycoprotein, Quantitative structure pharmacokinetic (property) relationshipAutores:Brito-Sánchez Y., Cherkasov A., Morell Pérez C., Stephen Jones Barigye, Thu H.L.T., Yaber-Goenaga I., Yovani Marrero-PonceFuentes:scopusQuantitative 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:googlescopusRetrained classification of tyrosinase inhibitors and "In Silico" potency estimation by using atom-type linear indices: A powerful tool for speed up the discovery of leads
Book PartAbstract: In this paper, the authors present an effort to increase the applicability domain (AD) by means of rPalabras claves:Autores:Abad C., García-Domenech R., Gerardo M. Casañola-Martin, Khan M.T.H., Rescigno A., Thu H.L.T., Torrens F., Yovani Marrero-PonceFuentes:scopus