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

• 作者: Bafétigué Ouattara Doh Soro Lynda Ekou Mamadou Guy-Richard Kone Nahossé Ziao Tchirioua Ekou
• 来源期刊: 计算分子生物学(英文)
• 学科: 医学
• 关键词: Hydroxamic ACIDS HISTONES DEACETYLASES DFT HISTONES DEACETYLASES Inhibitors QSAR
• 年，卷（期）: jsfzswxyw_2019,（3）
• 页码范围: 63-80
• 页数: 18页
• 分类号: R73

摘要

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 . Electronic affinity is the priority descriptor in predicting the activity of HDAC7 inhibitors in this study. The results obtained suggest that the descriptors derived from the DFT could be useful to predict the activity of histone deacetylase 7 inhibitors. These models were evaluated according to the criteria of Tropsha et al.