Quantitative structure–activity relationship model for prediction study of corrosion inhibition efficiency using two‐stage sparse multiple linear regression

A new quantitative structure–activity relationship (QSAR) of the inhibition of mild steel corrosion in 1 M hydrochloric acid using furan derivatives was developed by proposing two‐stage sparse multiple linear regression. The sparse multiple linear regression using ridge penalty and sparse multiple linear regression using elastic net (SMLRE) were used to develop the QSAR model. The results show that the SMLRE‐based model possesses high predictive power compared with sparse multiple linear regression using ridge penalty‐based model according to the mean‐squared errors for both training and test datasets, leave‐one‐out internal validation (Q²_int = 0.98), and external validation (Q²_ext = 0.95). In addition, the results of applicability domain assessment using the leverage approach reveal a reliable and robust SMLRE‐based model. In conclusion, the developed QSAR model using SMLRE can be efficiently used in the studies of corrosion inhibition efficiency. Copyright © 2016 John Wiley & Sons, Ltd.     

Structural studies and antileishmanial activity of 2‐acetylpyridine and 2‐benzoylpyridine nitroimidazole‐derived hydrazones

Three imidazole hydrazone compounds, namely 2‐(4‐nitro‐1H‐imidazol‐1‐yl)‐N′‐[1‐(pyridin‐2‐yl)ethylidene]acetohydrazide, C₁₂H₁₂N₆O₃, ( 1 ), 2‐(2‐nitro‐1H‐imidazol‐1‐yl)‐N′‐[1‐(pyridin‐2‐yl)ethylidene]acetohydrazide, C₁₂H₁₂N₆O₃, ( 2 ), and 2‐(2‐nitro‐1H‐imidazol‐1‐yl)‐N′‐[(phenyl)(pyridin‐2‐yl)methylidene]acetohydrazide, C₁₇H₁₄N₆O₃, ( 3 ), were obtained and fully characterized, including their crystal structure determinations. While all the compounds proved not to be cytotoxic to J774.A1 macrophage cells, ( 1 ) and ( 3 ) exhibited activity against Leishmania chagasi, whereas ( 2 ) was revealed to be inactive. Since both ( 1 ) and ( 3 ) exhibited antileishmanial effects, while ( 2 ) was devoid of activity, the presence of the acetyl or benzoyl groups was possibly not a determining factor in the observed antiprotozoal activity. In contrast, since ( 1 ) and ( 3 ) are 4‐nitroimidazole derivatives and ( 2 ) is a 2‐nitroimidazole‐derived compound, the presence of the 4‐nitro group probably favours antileishmanial activity over the 2‐nitro group. The results suggested that further investigations on compounds ( 1 ) and ( 3 ) as bioreducible antileishmanial prodrug candidates are called for.     

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R² and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R² test = 0.9643, RMSE test = 0.0155, R² train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.     

Ligand‐based design,virtual screening,and ADME/T‐based profiling on a dataset of 1,3,5‐triazine‐substituted benzene sulfonamides as carbonic anhydrase inhibitors

A quantitative structure–activity relationship (QSAR) analysis was performed on a dataset of 62 (1,3,5‐triazine‐substituted) benzene sulfonamides as carbonic anhydrase II and IX inhibitors using simulated annealing‐based multiple linear regression analysis. The selected QSAR model for carbonic anhydrase II inhibition (cross‐validated Q² = 0.689, , ) showed that aromaticity, lipophilicity, electronegativity, and molecular projection in the XZ plane influence the activity, whereas that for carbonic anhydrase IX inhibition (cross‐validated Q² = 0.767, , ) showed that activity was influenced by hydrophilicity, linker between the aromatic rings, electronegativity, and molecular weight. The QSAR model selected was internally and externally validated to define its predictability. Activity prediction of an external dataset containing nine compounds (within the same sphere of applicability) was performed to prove the models' specificity, selectivity, and sensitivity. The hypothesis in the form of the QSAR model was used for ligand‐based virtual screening on the ZINC database to obtain some potential hits. Similarly, docking studies on screened hits showed that the molecules interact and orient at the catalytic site in a way similar to acetazolamide. Additionally, an absorption, distribution, metabolism, excretion, and toxicity screening was also performed, and results showed that most of the compounds that can be possible drug candidates obey the Lipinski rule of five and Jorgensen rule of three. Copyright © 2013 John Wiley & Sons, Ltd.