Abdo Mohammed Al-Fakih

High‐dimensional QSAR prediction of anticancer potency of imidazo[4,5‐b]pyridine derivatives using adjusted adaptive LASSO

In high‐dimensional quantitative structure–activity relationship (QSAR) studies, identifying relevant molecular descriptors is a major goal. In this study, a proposed penalized method is used as a tool for molecular descriptors selection. The method, called adjusted adaptive least absolute shrinkage and selection operator (LASSO) (AALASSO), is employed to study the high‐dimensional QSAR prediction of the anticancer potency of a series of imidazo[4,5‐b]pyridine derivatives. This proposed penalized method can perform consistency selection and deal with grouping effects simultaneously. Compared with other commonly used penalized methods, such as LASSO and adaptive LASSO with different initial weights, the results show that AALASSO obtains the best predictive ability not only by consistency selection but also by encouraging grouping effects in selecting more correlated molecular descriptors. Hence, we conclude that AALASSO is a reliable penalized method in the field of high‐dimensional QSAR studies. Copyright

High-dimensional quantitative structure–activity relationship modeling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on a two-stage adaptive penalized rank regression

Outliers in the biological activity variable or the heavy tailed distribution of the error are often encountered in practice. Under these circumstances, the quantittative structure–activity relationship (QSAR) model using multiple linear regression is not efficient. In this paper, a two‐stage adaptive penalized rank regression is proposed for constructing a robust and efficient high‐dimensional QSAR model of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors. The results demonstrate the effectiveness of our proposed method in simultaneously estimating a robust QSAR model and selecting informative molecular descriptors. Furthermore, the results prove that the proposed method can significantly encourage the grouping effect. The proposed method, because of the high predictive ability and robustness, could be a useful method in high‐dimensional QSAR modeling. Copyright

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 (Q2int = 0.98), and external validation (Q2ext = 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

High-dimensional QSAR modelling using penalized linear regression model with L1/2-norm

Abstract In high-dimensional quantitative structure–activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.

High-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty

This study addresses the problem of the high‐dimensionality of quantitative structure‐activity relationship (QSAR) classification modeling. A new selection of descriptors that truly affect biological activity and a QSAR classification model estimation method are proposed by combining the sparse logistic regression model with a bridge penalty for classifying the anti‐hepatitis C virus activity of thiourea derivatives. Compared to other commonly used sparse methods, the proposed method shows superior results in terms of classification accuracy and model interpretation.

A sparse QSRR model for predicting retention indices of essential oils based on robust screening approach

Abstract A robust screening approach and a sparse quantitative structure–retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed modified robust sure independence screening (MR-SIS) method. Second, prediction of RIs was made using the proposed robust sparse QSRR with smoothly clipped absolute deviation (SCAD) penalty (RSQSRR). The RSQSRR model was internally and externally validated based on , , , , Y-randomization test, , , and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of the RSQSRR for training dataset outperform the other two used modelling methods. The RSQSRR shows the highest , , and , and the lowest . For the test dataset, the RSQSRR shows a high external validation value (), and a low value of compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed RSQSRR is an efficient approach for modelling high dimensional QSRRs and the method is useful for the estimation of RIs of essential oils that have not been experimentally tested.

A penalized quantitative structure–property relationship study on melting point of energetic carbocyclic nitroaromatic compounds using adaptive bridge penalty

Abstract A penalized quantitative structure–property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ( ) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ( ). The PBridge based model was internally and externally validated based on , , , , , , the Y-randomization test, , , , and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest of 0.959, of 0.953, of 0.949 and of 0.959, and the lowest and . For the test dataset, PBridge shows a higher of 0.945 and of 0.948, and a lower and , indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.