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.     

Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data

Quantitative structure–activity relationship (QSAR) techniques have found wide application in the fields of drug design, property modeling, and toxicity prediction of untested chemicals. A rigorous validation of the developed models plays the key role for their successful application in prediction for new compounds. The r_m² metrics introduced by Roy et al. have been extensively used by different research groups for validation of regression‐based QSAR models. This concept has been further advanced here with introduction of scaling of response data prior to computation of r_m². Further, a web application (accessible from http://aptsoftware.co.in/rmsquare/ and http://203.200.173.43:8080/rmsquare/ ) for calculation of the r_m² metrics has been introduced here. The present study reports that the web application can be easily used for computation of r_m² metrics provided observed and QSAR‐predicted data for a set of compounds are available. Further, scaling of response data is recommended prior to r_m² calculation. © 2013 Wiley Periodicals, Inc.     

Molecular modeling studies of human immunodeficiency virus type 1 protease inhibitors using three‐dimensional quantitative structure‐activity relationship,virtual screening,and docking simulations

In this paper, two 3‐dimensional quantitative structure‐activity relationship models for 60 human immunodeficiency virus (HIV)‐1 protease inhibitors were established using random sampling analysis on molecular surface and translocation comparative molecular field vector analysis (Topomer CoMFA). The non–cross‐validation (r²), cross‐validation (q²), correlation coefficient of external validation (Q²_ext), and F of 2 models were 0.94, 0.80, 0.79, and 198.84 and 0.94, 0.72, 0.75, and 208.53, respectively. The results indicated that 2 models were reasonable and had good prediction ability. Topomer Search was used to search R groups in the ZINC database, 20 new compounds were designed, and the Topomer CoMFA model was used to predicate the biological activity. The results showed that 18 new compounds were more active than the template molecule. So the Topomer Search is effective in screening and can guide the design of new HIV/AIDS drugs. The mechanism of action was studied by molecular docking, and it showed that the protease inhibitors and Ile50, Asp25, and Arg8 sites of HIV‐1 protease have interactions. These results have provided an insight for the design of new potent inhibitors of HIV‐1 protease.     

Quantitative Structure Property Relations （QSPR） for Predicting Molar Diamagnetic Susceptibilities,xm, of Inorganic Compounds

For predicting the molar diamagnetic susceptibilities of inorganic compounds, a novel connectivity index ^mG based on adjacency matrix of molecular graphs and ionic parameter gi was proposed. The gi is defined as gi=（ni^0.5-0.91）^4·xi^0.5｜Zi^0.5, where Zi, ni, xi are the valence, the outer electronic shell primary quantum number, and the electronegativity of atom i respectively. The good QSPR models for the molar diamagnetic susceptibilities can be constructed from ^0G and ^1G by using multivariate linear regression （MLR） method and artificial neural network （NN） method. The correlation coefficient r, standard error, and average absolute deviation of the MLR model and NN model are 0.9868, 5.47 cgs, 4.33 cgs, 0.9885, 5.09 cgs and 4.06 cgs, respectively, for the 144 inorganic compounds. The cross-validation by using the leave-one-out method demonstrates that the MLR model is highly reliable from the point of view of statistics. The average absolute deviations of predicted values of the molar diamagnetic susceptibility of other 62 inorganic compounds （test set） are 4.72 cgs and 4.06 cgs for the MLR model and NN model. The results show that the current method is more effective than literature methods for estimating the molar diamagnetic susceptibility of an inorganic compound. Both MLR and NN methods can provide acceptable models for the prediction of the molar diamagnetic susceptibilities. The NN model for the molar diamagnetic susceptibilities appears more reliable than the MLR model.     

Quantitative structure-toxicity relationship: An “in silico study” using electrophilicity and hydrophobicity as descriptors

To investigate the importance and suitability of quantitative structure-toxicity relationship approach in the field of aquatic toxicology, we have performed an extensive study introducing multiple linear regression (MLR) and multilayer perceptron neural network (MLP-NN) techniques. In this study, toxicity (pIGC₅₀) prediction of 169 aliphatic compounds toward Tetrahymena pyriformis (a freshwater protozoa) has been made by using all possible combinations of electrophilicity index (ω), square of electrophilicity index (ω²), cube of electrophilicity index (ω³), hydrophobicity (logP), and its square term {(logP)²} as predictors in the developed models. The MLR and MLP employed to construct the linear prediction models for the complete sets lead to a good correlation coefficient (R²) ranging from 0.703 to 0.779 in case of electronic factors (ω, ω², ω³) and 0.790 to 0.983 in case of lipophilic factors {logP, (logP)²}, respectively, except for amino alcohols. Furthermore, to cross-check the variable selection, a three-set cross-validation approach has been carried out. To demonstrate our overall result, the sum of ranking differences with ties has been evaluated considering the whole data set.     

Four Methods to Estimate Minimum Miscibility Pressure of CO2‐Oil Based on Machine Learning

CO₂ flooding accounts for a considerable proportion in gas flooding. Using CO₂ as a gas displacement agent is benefit for enhanced oil recovery (EOR), and the alleviation of the greenhouse effect by the permanent storage of CO₂ in the crust. Minimum miscibility pressure (MMP) of CO₂‐oil is a key factor affecting EOR, which determines the yield and economic benefit of crude oil recovery. Therefore, it is of great importance to use fast, accurate and cheap prediction methods for MMP estimation. In the present study, to evaluate the reliability of four recently developed prediction models based on machine learning (i.e., neural network analysis (NNA), genetic function approximation (GFA), multiple linear regression (MLR), partial least squares (PLS)), 136 sets of data are selected for calculation via outlier analysis from 147 sets of data. Afterwards, we compared the four models with existing prediction models from the literature. The analysis of correlation coefficients and multiple error functions shows that the four models can solve the MMP prediction problem well, and the model using intelligent algorithm has a higher prediction accuracy than the simple linear model. Besides, intelligent methods based on similarity algorithm have little difference from each other. Finally, a sensitivity analysis was conducted.