3D QSAR based on conceptual DFT molecular fields: Antituberculotic activity

Molecular design of high-efficacy and high drug safety Fluoroquinolones suitable for a variety of aerobic biodegradation bacteria

The present study attempted to improve the biodegradation removal rate of Fluoroquinolones (FQs) in sewage treatment plants. The similarity index analysis (CoMSIA) model for combined biodegradability was constructed, and 33 kinds of molecular derivatives of FQs suitable for a variety of aerobic biodegradation microorganisms were designed. Further, derivative-20 and derivative-28, with high drug efficiency, drug safety, and environmental friendliness were selected through pharmacokinetics (ADMET), toxicokinetics (TOPKAT), FQs functional characteristics, and environmental friendliness evaluations. Compared with the target molecules, the combined biodegradability of the above two FQ-derivative molecules were increased by 193.57 % and 205.07 %, respectively, while their environment-friendly characteristics were improved to a certain degree. Through molecular docking and molecular dynamic simulation analysis, it showed that van der Waals force (decreased by 2.73 %-61.74 %) was the main factor influencing the binding ability of the modified FQ molecules to the receptor proteins. In addition, the relationship among the non-bonding interaction resultant force, the binding effect of the FQ-derivative molecules, and the receptor protein-related amino acid residues were studied for the first time. It was observed that the higher the value of the non-bonding interaction resultant force, the better was the binding effect, which demonstrating the significantly improved biodegradability of the designed FQ-derivative molecules.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

Background:

The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes.

Methods:

In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors.

Results:

The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities.

Conclusion:

Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs

Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R2) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal mol-1 for the training set. The best PLS model includes six latent variables, and has an R2 value of 0.91 and a RMSE of 0.52 kcal mol-1. The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.

Theoretical Reactivity Study of Indol-4-Ones and Their Correlation with Antifungal Activity

Chemical reactivity descriptors of indol-4-ones obtained via density functional theory (DFT) and hard–soft acid–base (HSAB) principle were calculated to prove their contribution in antifungal activity. Simple linear regression was made for global and local reactivity indexes. Results with global descriptors showed a strong relationship between antifungal activity vs. softness (S) (r = 0.98) for series I (6, 7a–g), and for series II (8a–g) vs. chemical potential (µ), electronegativity (χ) and electrophilicity (ω) (r = 0.86), p < 0.05. Condensed reactivity descriptors s_k⁺, ω_k⁻ for different fragments had strong relationships for series I and II (r = 0.98 and r = 0.92). Multiple linear regression was statistically significant for S (r = 0.98), η (r = 0.91), and µ/ω (r = 0.91) in series I. Molecular electrostatic potential maps (MEP) showed negative charge accumulation around oxygen of carbonyl group and positive accumulation around nitrogen. Fukui function isosurfaces showed that carbons around nitrogen are susceptible to electrophilic attack, whereas the carbon atoms of the carbonyl and phenyl groups are susceptible to nucleophilic attack for both series. The above suggest that global softness in conjunction with softness and electrophilicity of molecular fragments in enaminone systems and pyrrole rings contribute to antifungal activity of indol-4-ones.

Quantitative Structure-Activity/Property/Toxicity Relationships through Conceptual Density Functional Theory-Based Reactivity Descriptors

Developing effective structure-activity/property/toxicity relationships (QSAR/QSPR/QSTR) is very helpful in predicting biological activity, property, and toxicity of a given set of molecules. Regular change in these properties with the structural alteration is the main reason to obtain QSAR/QSPR/QSTR models. The advancement in making different QSAR/QSPR/QSTR models to describe activity, property, and toxicity of various groups of molecules is reviewed in this chapter. The successful implementation of Conceptual Density Functional Theory (CDFT)-based global as well as local reactivity descriptors in modeling effective QSAR/QSPR/QSTR is highlighted.

Atom and Bond Fukui Functions and Matrices: A Hirshfeld-I Atoms-in-Molecule Approach

The Fukui function is often used in its atom-condensed form by isolating it from the molecular Fukui function using a chosen weight function for the atom in the molecule. Recently, Fukui functions and matrices for both atoms and bonds separately were introduced for semiempirical and ab initio levels of theory using Hückel and Mulliken atoms-in-molecule models. In this work, a double partitioning method of the Fukui matrix is proposed within the Hirshfeld-I atoms-in-molecule framework. Diagonalizing the resulting atomic and bond matrices gives eigenvalues and eigenvectors (Fukui orbitals) describing the reactivity of atoms and bonds. The Fukui function is the diagonal element of the Fukui matrix and may be resolved in atom and bond contributions. The extra information contained in the atom and bond resolution of the Fukui matrices and functions is highlighted. The effect of the choice of weight function arising from the Hirshfeld-I approach to obtain atom- and bond-condensed Fukui functions is studied. A comparison of the results with those generated by using the Mulliken atoms-in-molecule approach shows low correlation between the two partitioning schemes.

Analyzing the substitution effect on the CoMFA results within the framework of density functional theory (DFT)

Though QSAR was originally developed in the context of physical organic chemistry, it has been applied very extensively to chemicals (drugs) which act on biological systems, in this idea one of the most important QSAR methods is the 3D QSAR model. However, due to the complexity of understanding the results it is necessary to postulate new methodologies to highlight their physical-chemical meaning. In this sense, this work postulates new insights to understand the CoMFA results using molecular quantum similarity and chemical reactivity descriptors within the framework of density functional theory. To obtain these insights a simple theoretical scheme involving quantum similarity (overlap, coulomb operators, their euclidean distances) and chemical reactivity descriptors such as chemical potential (μ), hardness (ɳ), softness (S), electrophilicity (ω), and the Fukui functions, was used to understand the substitution effect. In this sense, this methodology can be applied to analyze the biological activity and the stabilization process in the non-covalent interactions on a particular molecular set taking a reference compound.

3D-QSAR based on quantum-chemical molecular fields: toward an improved description of halogen interactions

Current 3D-QSAR methods such as CoMFA or CoMSIA make use of classical force-field approaches for calculating molecular fields. Thus, they can not adequately account for noncovalent interactions involving halogen atoms like halogen bonds or halogen-π interactions. These deficiencies in the underlying force fields result from the lack of treatment of the anisotropy of the electron density distribution of those atoms, known as the "σ-hole", although recent developments have begun to take specific interactions such as halogen bonding into account. We have now replaced classical force field derived molecular fields by local properties such as the local ionization energy, local electron affinity, or local polarizability, calculated using quantum-mechanical (QM) techniques that do not suffer from the above limitation for 3D-QSAR. We first investigate the characteristics of QM-based local property fields to show that they are suitable for statistical analyses after suitable pretreatment. We then analyze these property fields with partial least-squares (PLS) regression to predict biological affinities of two data sets comprising factor Xa and GABA-A/benzodiazepine receptor ligands. While the resulting models perform equally well or even slightly better in terms of consistency and predictivity than the classical CoMFA fields, the most important aspect of these augmented field-types is that the chemical interpretation of resulting QM-based property field models reveals unique SAR trends driven by electrostatic and polarizability effects, which cannot be extracted directly from CoMFA electrostatic maps. Within the factor Xa set, the interaction of chlorine and bromine atoms with a tyrosine side chain in the protease S1 pocket are correctly predicted. Within the GABA-A/benzodiazepine ligand data set, PLS models of high predictivity resulted for our QM-based property fields, providing novel insights into key features of the SAR for two receptor subtypes and cross-receptor selectivity of the ligands. The detailed interpretation of regression models derived using improved QM-derived property fields thus provides a significant advantage by revealing chemically meaningful correlations with biological activity and helps in understanding novel structure-activity relationship features. This will allow such knowledge to be used to design novel molecules on the basis of interactions additional to steric and hydrogen-bonding features.