Cross-validated Regression Coefficient Research Articles

Development of Predictive Quantitative Structure-Activity Relationship Models of Epipodophyllotoxin Derivatives

Epipodophyllotoxins are the most important anticancer drugs used in chemotherapy for various types of cancers. To further, improve their clinical efficacy a large number of epipodophyllotoxin derivatives have been synthesized and tested over the years. In this study, a quantitative structure-activity relationship (QSAR) model has been developed between percentage of cellular protein-DNA complex formation and structural properties by considering a data set of 130 epipodophyllotoxin analogues. A systematic stepwise searching approach of zero tests, missing value test, simple correlation test, multicollinearity test, and genetic algorithm method of variable selection was used to generate the model. A statistically significant model (r((train))(2) = 0.721; q(cv)(2) = 0.678) was obtained with descriptors such as solvent-accessible surface area, heat of formation, Balaban index, number of atom classes, and sum of E-state index of atoms. The robustness of the QSAR models was characterized by the values of the internal leave-one-out cross-validated regression coefficient (q(cv)(2)) for the training set and r((test))(2) for the test set. The root mean square error between the experimental and predicted percentage of cellular protein-DNA complex formation (PCPDCF) was 0.194 and r((test))(2) = 0.689, revealing good predictability of the QSAR model.

Molecular Modeling and Structure-Activity Relationship of Podophyllotoxin and Its Congeners

A quantitative structure-activity relationship (QSAR) model has been developed between cytotoxic activity and structural properties by considering a data set of 119 podophyllotoxin analogs based on 2D and 3D structural descriptors. A systematic stepwise searching approach of zero tests, a missing value test, a simple correlation test, a multicollinearity test, and a genetic algorithm method of variable selection was used to generate the model. A statistically significant model (r(train)(2) = 0.906; q(cv)(2) = 0.893) was obtained with the molecular descriptors. The robustness of the QSAR model was characterized by the values of the internal leave-one-out cross-validated regression coefficient (q(cv)(2)) for the training set and r(test)(2) for the test set. The overall root mean square error (RMSE) between the experimental and predicted pIC(50) value was 0.265 and r(test)(2) = 0.824, revealing good predictability of the QSAR model. For an external data set of 16 podophyllotoxin analogs, the QSAR model was able to predict the tubulin polymerization inhibition and mechanistically cytotoxic activity with an RMSE value of 0.295 in comparison to experimental values. The QSAR model developed in this study shall aid further design of novel potent podophyllotoxin derivatives.

A DFT-based QSAR study of the toxicity of quaternary ammonium compounds on Chlorella vulgaris

The DFT-based descriptors were used to derive the quantitative structure–activity relationship (QSAR) models enabling the calculated quantum chemistry parameters to be correlated to the toxicity of quaternary ammonium compounds (QACs) on green alga Chlorella vulgaris. DFT/B3LYP level of theory with the 6-31G(d) basis set was applied to calculate a set of quantum chemistry descriptors for 11 QACs. The partial least squares (PLS) analysis implemented in Simca-P was employed to obtain the QSAR models. The optimal PLS model with the cumulative cross-validated regression coefficient ( Q cum 2 = 0.893 ) and the correlation coefficient between observed values and fitted values ( R = 0.975) explained 95.3% of the variance of the independent variables and 92.8% of the variance of the dependent variable. The results of this investigation show that alkyl chain lengths ( CL), polarizability tense ( α zz ), the most positive net atomic charges on a hydrogen atom ( q H + ) and entropy ( S o) are the major descriptors in governing the log(1/EC 50) values of the QACs.

Modelling the depuration rates of polychlorinated biphenyls in Oncorhynchus mykiss with quantum chemical descriptors

Using quantum chemical descriptors and partial least squares regression, a quantitative structure–activity relationship (QSAR) model is developed for the depuration rate constants (log k d) of 62 polychlorinated biphenyls (PCBs) in juvenile rainbow trout (Oncorhynchus mykiss). The values of the cross-validated regression coefficient () and standard deviation (SD) are 0.655 and 0.05, respectively. The high cross-validated coefficient and low standard deviation indicate that the QSAR model is well predictive. In the QSAR model, the following six descriptors are highly significant: (the most positive charge of a hydrogen atom), HOF (standard heat of formation), CCR (core–core repulsion), EE (electronic energy), α2 (squared average molecular polarisability), and S (molecular surface area). The significant descriptors show that the depuration of PCBs in rainbow trout may be mainly attributed to the biota–water partitioning process, and the reactive activity of PCB molecules may play a subordinate role.

A QSAR Model for Predicting Mutagenicity of Nitronaphthalenes and Methylnitronaphthalenes

A quantitative structure-activity relationship model for prediction of mutagenicity of nitronaphthalenes and methylnitronaphthalenes was developed using some fundamental quantum chemical descriptors. The cumulative cross-validated regression coefficient value for the optimal quantitative structure-activity relationship model is 0.711, showing a good predictive capability for mutagenicity of nitronaphthalenes and methylnitronaphthalenes. Results from this study indicate that mutagenicity of nitronaphthalenes and methylnitronaphthalenes increases with increasing frontier molecular orbital energy value, i.e., the sum of the energy of the highest occupied molecular orbital and the energy of the lowest unoccupied molecular orbital, or decreasing the energy of the second highest occupied molecular orbital, final heat of formation, and core-core repulsion energy values.

3D-QSAR study on atmospheric half-lives of POPs using CoMFA and CoMSIA

3D-QSAR studies of persistent organic pollutants (POPs) screening for atmosphere persistence were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods. The mean and maximum half-life estimations for degradation in air of 49 UNEP POPs and possible POPs were modeled. Both groups' data have been modeled to obtain an average estimate and a predictive value for ranking and screening purposes. CoMFA and CoMSIA models have given cross-validation regression coefficient (q2) values of more than 0.69 and correlation coefficient (r2) value of more than 0.84, which validated for their prediction, could be applied to predict unavailable data.

Holographic QSAR of selected esters

The HQSAR (Holographic QSAR) method, which has been recently developed, can offer the ability to rapidly and easily generate QSAR models of high statistical quality and predictive value. HQSAR analysis requires selecting values for parameters that specify the size of the hologram that is to be used, and the size and type of fragment substructures that are to be encoded. The color coding is provided by HQSAR to reflect which molecular fragments may be important contributors to the biological activity. In this work, we studied the quantitative structure activity relationship of selected esters using the HQSAR method. A robust HQSAR model with r 2 (non-cross-validated regression coefficient) of 0.981 and q 2 (cross-validated regression coefficient) of 0.912, was developed after optimizing the fragment size and the hologram length. The color coding analysis, which has rarely been reported before, was done here to explain the outlier successfully.

Sorption of sulfonamide pharmaceutical antibiotics on whole soils and particle-size fractions.

Residues of pharmaceutical antibiotics are found in the environment, whose fate and effects are governed by sorption. Thus, the extent and mechanisms of the soil sorption of p-aminobenzoic acid and five sulfonamide antibiotics (sulfanilamide, sulfadimidine, sulfadiazine, sulfadimethoxine, and sulfapyridine) were investigated using topsoils of fertilized and unfertilized Chernozem and their organic-mineral particle-size fractions. Freundlich adsorption coefficients (K(f)) ranged from 0.5 to 6.5. Adsorption increased with aromaticity and electronegativity of functional groups attached to the sulfonyl-phenylamine core. Adsorption to soil and particle-size fractions increased in the sequence: coarse silt < whole soil < medium silt < sand < clay < fine silt and was influenced by pH. Sorption nonlinearity (1/n </= 0.76) indicated specific interactions with functional groups of soil organic matter (SOM). Phenolic and carboxylic groups, N-heterocyclic compounds, and lignin decomposition products were tentatively assigned as preferred binding sites using statistical analysis of pyrolysis-mass spectra and adsorption coefficients. Adsorption of sulfonamides to mineral soil colloids was weaker and resulted in a stronger desorption from clay-size fractions. Moreover, steric accessibility of organic-mineral complexes in clay-size fractions was significantly reduced. With a quantitative structure-property relationship (QSPR) model, combining the organic carbon concentration, the sulfonamides' chromatographic capacity factor (k'), and nondissociated species concentration (CF(a)), distribution coefficients (K(d)) were estimated with a cross-validated regression coefficient Q(2) = 0.71. Modeling and molecular mechanics calculations of antibiotic-SOM complexes showed preferred site-specific sorption via hydrogen bonds and van der Waals interactions. Distinct chemical structural properties, such as aromaticity and van der Waals volumes, correlated with the sorption data.