Theoretical Studies of QSAR and Molecular Design on a Novel Series of Ethynyl-3-Quinolinecarbonitriles as Src Inhibitors

QSAR model and mechanism research on color removal efficiency of dying wastewater by FeCl3 coagulation

Coagulation is the most widely used method in the treatment of printing and dying wastewater. To better understand the relationship between the coagulation effect and dye molecular structures, quantitative structure activity relationship (QSAR) analyses were performed to elucidate the factors affecting the coagulation in ferric chloride (FeCl₃) coagulation process. First, the coagulation experiments on 38 dye molecules were conducted to determine their color removal rates (R_exp) by FeCl₃ under different pH conditions (i.e., pH = 4 and 10). The results showed that the average R_exp of dyes were 41.36% ± 2.40% at pH value of 4 and 55.70% ± 2.83% at pH value of 10. Subsequently, a multiple linear regression (MLR) method was used to construct QSAR models based on R_exp and 42 molecular parameters calculated by Gaussian 09, Materials Studio 7.0 and Multiwfn. The developed QSAR models exhibited excellent stability, reliability, and robustness with values of R² = 0.7950, 0.8170, Q²_INT = 0.6401, 0.7382, Q²_EXT = 0.5168, 0.5441, at pH values of 4 and 10, respectively. Through analysis of quantum parameter values, electrostatic adsorption and hydrogen bonding adsorption were primarily responsible for the coagulation process. Therefore, this study could be useful in providing critical information for evaluating the removal efficiency and a feasible way to predict the removal rate of dyes by FeCl₃ when no coagulation experiments were conducted.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.