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

Abstract

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 R 2 = 0.9659, S = 0.488, F = 85 and p-value < 0.0001. 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.

Correlation Between the Electronic Structure of Quinazoline Derivatives and the Activity of the Ntr1 Receptor

“…Most of the local OM molecules coincides with molecular HOMO, which indicates a high reactivity of the local OM. It is suggested that atom 14 is interacting with a deficient center in electrons through its first two busiest local molecular orbitals [20,21].…”

Thermal Conductivity Estimation of Diverse Liquid Aliphatic Oxygen-Containing Organic Compounds Using the Quantitative Structure–Property Relationship Method

“…The AD is determined by the response (property) of the compounds and the descriptors in the developed QSPR model . The Williams plot with standardized cross validated residuals ( R ) versus leverage (Hat diagonal) values ( h ) is highly recommended, which is widely used for visualizing the AD. − It should be noted that if the h value of a compound in the training set is greater than the threshold value h * ( h * = 3 p / n , where p is the number of model variables plus one and n is the number of the training set data), the structure of this compound reinforces the developed model. , If the most data points are located in the region of 0 ≤ h ≤ h * and −3 ≤ R ≤ 3, the developed model can be considered statistically acceptable and valid.…”

“…55−57 It should be noted that if the h value of a compound in the training set is greater than the threshold value h* (h* = 3p/n, where p is the number of model variables plus one and n is the number of the training set data), the structure of this compound reinforces the developed model. 10,58 If the most data points are located in the region of 0 ≤ h ≤ h* and −3 ≤ R ≤ 3, the developed model can be considered statistically acceptable and valid.…”