DFT Study, Linear and Nonlinear Multiple Regression in the Prediction of HDAC7 Inhibitory Activities on a Series of Hydroxamic Acids

Autor:	Nahossé Ziao, Lynda Ekou, Tchirioua Ekou, Bafétigué Ouattara, Doh Soro, Mamadou Guy-Richard Koné
Rok vydání:	2019
Předmět:	Quantitative structure–activity relationship Series (mathematics) Chemistry Computational chemistry Principal component analysis Molecule Multiple linear regression analysis Density functional theory General Medicine Nonlinear multiple regression Multivariate statistical
Zdroj:	Computational Molecular Bioscience. :63-80
ISSN:	2165-3453 2165-3445
DOI:	10.4236/cmb.2019.93006
Popis:	In this work, we conducted a QSAR study on 18 molecules using descriptors from the Density Functional Theory (DFT) in order to predict the inhibitory activity of hydroxamic acids on histone deacetylase 7. This study is performed using the principal component analysis (PCA) method, the Ascendant Hierarchical Classification (AHC), the linear multiple regression method (LMR) and the nonlinear multiple regression (NLMR). DFT calculations were performed to obtain information on the structure and information on the properties on a series of hydroxamic acids compounds studied. Multivariate statistical analysis yielded two quantitative models (model MLR and model MNLR) with the quantum descriptors: electronic affinity (AE), vibration frequency of the OH bond (ν(OH)) and that of the NH bond (ν(NH)). The LMR model gives statistically significant results and shows a good predictability R2 = 0.9659, S = 0.488, F = 85 and p-value et al.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b86710900b588efe4dbca29604fa3e2c https://doi.org/10.4236/cmb.2019.93006 Zobrazit plný text záznamu