QSAR analysis of 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines exhibiting anticancer activity by optimal SMILES-based descriptors

A predictive model of the anticarcinogenic activity of a 1,4-dihydro-4-oxo-1-(2-thiazolyl)-1,8-naphthyridines series has been built, with statistical quality: n = 75, r ² = 0.7688, s = 0.48, F = 243 (training set); n = 25, r ² = 0.8025, s = 0,49, F = 93 (test set). The robustness of this model has been tested in three random splits into training set and test set. Correlation weights (the analogue of the contributions of substituents) of molecular attributes expressed by symbols in the simplified molecular input line entry system (SMILES) notation are able to serve as informative indicators in the search for new anticancer agents.     

CoMFA and CoMSIA studies on a new series of xanthone derivatives against the oral human epidermoid carcinoma (KB) cancer cell line

Abstract  

A new series of xanthone derivatives against the oral human epidermoid carcinoma (KB) cancer cell line is examined to determine the relationship between the structural properties and the biological activity of these compounds—the 3-D quantitative structure–activity relationship (3D-QSAR)—using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMFA and CoMSIA models were obtained using the atom-based alignment of 33 compounds, 22 training compounds and 11 tested compounds, and these give desirable statistics; those for the CoMFA standard model were: r _cv² = 0.691, r ² = 0.998, S _press = 0.178, s = 0.014 and F = 1080.765, while CoMSIA combined steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields: r _cv² = 0.600, r ² = 0.988, S _press = 0.206, s = 0.034 and F = 284.433. The 3D-QSAR models calculated satisfactory test set activities. The 3D-QSAR contour plots correlated strongly with the experimental data for the binding topology. For this reason, these results would be beneficial for predicting affinities with the compounds of interest, and they are advantageous for guiding the design and synthesis of new and more effective anticancer agents.     

CoMFA and CoMSIA studies on a new series of xanthone derivatives against the oral human epidermoid carcinoma (KB) cancer cell line

Abstract  A new series of xanthone derivatives against the oral human epidermoid carcinoma (KB) cancer cell line is examined to determine the relationship between the structural properties and the biological activity of these compounds—the 3-D quantitative structure–activity relationship (3D-QSAR)—using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMFA and CoMSIA models were obtained using the atom-based alignment of 33 compounds, 22 training compounds and 11 tested compounds, and these give desirable statistics; those for the CoMFA standard model were: r _cv² = 0.691, r ² = 0.998, S _press = 0.178, s = 0.014 and F = 1080.765, while CoMSIA combined steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields: r _cv² = 0.600, r ² = 0.988, S _press = 0.206, s = 0.034 and F = 284.433. The 3D-QSAR models calculated satisfactory test set activities. The 3D-QSAR contour plots correlated strongly with the experimental data for the binding topology. For this reason, these results would be beneficial for predicting affinities with the compounds of interest, and they are advantageous for guiding the design and synthesis of new and more effective anticancer agents. Graphical abstract   A new and more effective anticancer agent of xanthone derivatives against the oral human epidermoid carcinoma (KB) cell line, as investigated by CoMFA and CoMSIA analysis     

QSPR Modeling of Gibbs Free Energy of Organic Compounds by Weighting of Nearest Neighboring Codes

We examine the encoding of chemical structure of organic compounds by Labeled Hydrogen-Filled Graphs (LHFGs). Quantitative Structure-Property Relationships (QSPR) for a representative set of 150 organic molecules have been derived by means of the optimization of correlation weights of local invariants of the LHFGs. We have tested as local invariants Morgan extended connectivity of zero- and first order, numbers of path of length 2 (P2) and valence shells of distance of 2 (S2) associated with each atom in the molecular structure, and the Nearest Neighboring Codes (NNC). The best statistical characteristics for the Gibbs free energy has been obtained for the NNC weighting. Statistical parameters corresponding to this model are the following n = 100, r² = 0.9974, s = 5.136 kJ/mol, F = 38319 (training set); n = 50, r² = 0.9990, s = 3.405 kJ/mol, F = 48717 (test set). Some possible further developments are pointed out.     

Prediction of drug solubility from molecular structure using a drug-like training set

Using a training set of 191 drug-like compounds extracted from the AQUASOL database a quantitative structure-property relationship (QSPR) study was conducted employing a set of simple structural and physicochemical properties to predict aqueous solubility. The resultant regression model comprised five parameters (ClogP, molecular weight, indicator variable for aliphatic amine groups, number of rotatable bonds and number of aromatic rings) and demonstrated acceptable statistics (r ² = 0.87, s = 0.51, F = 243.6, n = 191). The model was applied to two test sets consisting of a drug-like set of compounds (r ² = 0.80, s = 0.68, n = 174) and a set of agrochemicals (r ² = 0.88, s = 0.65, n = 200). Using the established general solubility equation (GSE) on the training and drug-like test set gave poorer results than the current study. The agrochemical test set was predicted with equal accuracy using the GSE and the QSPR equation. The results of this study suggest that increasing molecular size, rigidity and lipophilicity decrease solubility whereas increasing conformational flexibility and the presence of a non-conjugated amine group increase the solubility of drug-like compounds. Indeed, the proposed structural parameters make physical sense and provide simple guidelines for modifying solubility during lead optimisation.     

The pressure effect study on the burning rate of ammonium nitrate-HTPB-based propellant with the influence catalysts

The burning rate of AN–HTPB-based propellant catalysed with chromium salt has been studied using conventional strand burner under the various pressure range, i.e. from atmospheric pressure to 6.897 MPa and verified with Piobert law, i.e. r = aP ⁿ . At atmospheric pressure, the burning rate AN–HTPB propellant was being accelerated with the chromium-based catalysts used. In case of lead chromate-catalysed system, burning rate was observed 2.655 times higher than burning rate (r = 0.200 mm s⁻¹) of virgin AN–HTPB propellant sample. However, the Copper chromate-catalysed propellant burned with slower rate (r = 0.160 mm s⁻¹) than the virgin AN–HTPB propellant sample. The burning rate of all catalysed propellant samples are found to be the pressure sensitive and accelerated higher with rise of pressure. The highest burning rate (r = 2.422 mm s⁻¹) was recorded with ammonium dichromate and lowest (r = 1.40 mm s⁻¹) with lead chromate-catalysed propellant sample with the rise of pressure up to 6.897 MPa at different pressures. A linear relationship was observed between the burning rate and pressure rise which followed the Piobert law, i.e. r = aP ⁿ . The pressure index (n) values of AN–HTPB-based samples were calculated higher when catalysed with ammonium dichromate, Copper Chromate, Cr₂O₃, Potassium dichromate (n = 0.525, 0.555, 0.429, and 0.408 respectively) and lower (n = 0.226) with lead chromate compared to virgin sample (n = 0.405). Higher value indicates the positive effect on accelerating the burning rate with catalyst at higher pressure ranges.     

QM/MM based 3D QSAR models for potent B-Raf inhibitors

Three dimensional (3D) quantitative structure-activity relationship studies of 37 B-Raf inhibitors, pyrazole-based derivatives, were performed. Based on the co-crystallized compound (PDB ID: 3D4Q), several alignment methods were utilized to derive reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. Receptor-guided alignment with quantum mechanics/molecular mechanics (QM/MM) minimization led to the best CoMFA model (q ² = 0.624, r ² = 0.959). With the same alignment, a statistically reliable CoMSIA model with steric, H-bond acceptor, and hydrophobic fields was also derived (q ² = 0.590, r ² = 0.922). Both models were validated with an external test set, which gave satisfactory predictive r ² values of 0.926 and 0.878, respectively. Contour maps from CoMFA and CoMSIA models revealed important structural features responsible for increasing biological activity within the active site and explained the correlation between biological activity and receptor-ligand interactions. New fragments were identified as building blocks which can replace R1-3 groups through combinatorial screening methods. By combining these fragments a compound with a high bioactivity level prediction was found. These results can offer useful information for the design of new B-Raf inhibitors.