Suggestion of active 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents using predictive QSAR model based on the combination of ALASSO with an ANN model.

The novel severe acute respiratory syndrome coronavirus (SARS CoV-2) was introduced as an epidemic in 2019 and had millions of deaths worldwide. Given the importance of this disease, the recommendation and design of new active compounds are crucial. 3-chymotrypsin-like protease (3 CLpro) inhibitors have been identified as potent compounds for treating SARS-CoV-2 disease. So, the design of new 3 CLpro inhibitors was proposed using a quantitative structure-activity relationship (QSAR) study. In this context, a powerful adaptive least absolute shrinkage and selection operator (ALASSO) penalized variable selection method with inherent advantages coupled with a nonlinear artificial neural network (ANN) modelling method were used to provide a QSAR model with high interpretability and predictability. After evaluating the accuracy and validity of the developed ALASSO-ANN model, new compounds were proposed using effective descriptors, and the biological activity of the new compounds was predicted. Ligand-receptor (LR) interactions were also performed to confirm the interaction strength of the compounds using molecular docking (MD) study. The pharmacokinetics properties and calculated Lipinski's rule of five were applied to all proposed compounds. Due to the ease of synthesis of these suggested new compounds, it is expected that they have acceptable pharmacological properties.

An on-line spectrophotometric determination of trace amounts of thiourea in tap water, orange juice, and orange peel samples using multi-channel flow injection analysis.

In this work, a flow injection analysis (FIA) method was introduced for the determination of trace amounts of thiourea in tap water. This method is based upon the inhibition effect of thiourea on the reaction between meta-cresol purple (MCP) and potassium bromate catalyzed by bromide ions in a sulfuric acid medium. In the presence of thiourea, an induction period appears in the reaction system, and as a result, the absorbance of MCP increases at 525 nm in the FIA manifold. The chemical and FIA variables are studied and optimized using the univariate and Simplex optimization methods. Under the optimum conditions, thiourea can be determined in the range of 0.100-13.0 µg mL(-1). The limit of detection (3σ) for thiourea was found to be 0.0310 µg mL(-1). The relative standard deviations (RSDs) for six replicate determinations of 0.500, 5.00, and 12.0 µg mL(-1) of thiourea were 4.0%, 1.8%, and 1.2%, respectively. The proposed method was also applied for the determination of thiourea in orange juice and orange peel samples with recoveries in the range of 98.0-101%. The analytical speed of the method was calculated to be about 120 sample per hour.

Application of random forests method to predict the retention indices of some polycyclic aromatic hydrocarbons.

In this work, a quantitative structure-retention relationship (QSRR) investigation was carried out based on the new method of random forests (RF) for prediction of the retention indices (RIs) of some polycyclic aromatic hydrocarbon (PAH) compounds. The RIs of these compounds were calculated using the theoretical descriptors generated from their molecular structures. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. Optimization of these parameters showed that in the point m=70, nt=460, the RF method can give the best results. Also, performance of the RF model was compared with that of the artificial neural network (ANN) and multiple linear regression (MLR) techniques. The results obtained show the relative superiority of the RF method over the MLR and ANN ones.

Sequential eluent injection technique as a new approach for the on-line enrichment and speciation of Cr(III) and Cr(VI) species on a single column with FAAS detection.

This paper introduces a sequential eluent injection (SEI) technique combined with an on-line preconcentration/separation system for a fast and sensitive FAAS determination of trace amounts of Cr(III) and Cr(VI) species. The method is based on the simultaneous retention of Cr(III) and Cr(VI) on a single mini-column packed with a chloromethylated polystyrene functionalized with N,N-bis(naphthylideneimino)diethylenetriamine (PS-NAPdien) at pH 6.7. The retained chromium species was eluted by sequential injection of HCl for desorption of Cr(III), and NH(3) and NH(4)NO(3) buffer solutions for desorption of Cr(VI). All the chemical and flow injection variables were optimized for the quantitative preconcentration and speciation of Cr(III) and Cr(VI). Under the optimum conditions, the calibration graph obtained is linear over the concentration range of 2.0-60.0µg L(-1) for Cr(III), and 8.0-180.0µg L(-1) for Cr(VI). The preconcentration factors for Cr(III) and Cr(VI) were 70 and 30, respectively. The 3σ detection limits were 0.6µg L(-1) and 2.5µg L(-1) for Cr(III) and Cr(VI), respectively. The relative standard deviations were 2.55% and 0.8%, respectively, for 6 replicate determinations of Cr(III) and Cr(VI) at the 40.0µg L(-1) level. The proposed method was applied for determination of Cr(III) and Cr(VI) in different water samples with satisfactory results.