Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme.

The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r2 = 0.928-0.988, = 0.894-0.954, RMSE = 0.002-0.412, s = 0.001-0.214), and the predicted pKi values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r2 = 0.967, = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q2 = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.

In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach.

Certain drugs are nitroaromatic compounds, which are potentially toxic. As such, it is of practical importance to assess and predict their mutagenic potency in the process of drug discovery. A classical quantitative structure-activity relationship (QSAR) model was developed using the linear partial least square (PLS) scheme to understand the underline mutagenic mechanism and a non-classical QSAR model was derived using the machine learning-based hierarchical support vector regression (HSVR) to predict the mutagenicity of nitroaromatic compounds based on a series of mutagenicity data (TA98-S9). It was observed that HSVR performed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical validations. A mock test designated to mimic real challenges also confirmed the better performance of HSVR. Furthermore, HSVR exhibited superiority in predictivity, generalization capabilities, consistent performance, and robustness when compared with various published predictive models. PLS, conversely, revealed some mechanistically interpretable relationships between descriptors and mutagenicity. Thus, this two-QSAR approach using the predictive HSVR and interpretable PLS models in a synergistic fashion can be adopted to facilitate drug discovery and development by designing safer drug candidates with nitroaromatic moiety.

In silico prediction of inhibition of promiscuous breast cancer resistance protein (BCRP/ABCG2).

BACKGROUND: Breast cancer resistant protein has an essential role in active transport of endogenous substances and xenobiotics across extracellular and intracellular membranes along with P-glycoprotein. It also plays a major role in multiple drug resistance and permeation of blood-brain barrier. Therefore, it is of great importance to derive theoretical models to predict the inhibition of both transporters in the process of drug discovery and development. Hitherto, very limited BCRP inhibition predictive models have been proposed as compared with its P-gp counterpart. METHODOLOGY/PRINCIPAL FINDINGS: An in silico BCRP inhibition model was developed in this study using the pharmacophore ensemble/support vector machine scheme to take into account the promiscuous nature of BCRP. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those molecules in the training set (n= 22, r2 =0.82, qCV2=0.73, RMSE= 0.40, s = 0.24), test set (n =97, q2=0.75-0.89, RMSE= 0.31, s= 0.21), and outlier set (n= 16, q2 =0.72-0.91, RMSE= 0.29, s=0.17). When subjected to a variety of statistical validations, the developed PhE/SVM model consistently met the most stringent criteria. A mock test by HIV protease inhibitors also asserted its predictivity. CONCLUSIONS/SIGNIFICANCE: It was found that this accurate, fast, and robust PhE/SVM model can be employed to predict the BCRP inhibition of structurally diverse molecules that otherwise cannot be carried out by any other methods in a high-throughput fashion to design therapeutic agents with insignificant drug toxicity and unfavorable drug-drug interactions mediated by BCRP to enhance clinical efficacy and/or circumvent drug resistance.

Predicting activation of the promiscuous human pregnane X receptor by pharmacophore ensemble/support vector machine approach.

The nuclear receptor human pregnane X receptor (hPXR) is a ligand-regulated transcription factor that responds to a wide range of endogenous and xenobiotic molecules. Upon activation with ligands, hPXR can increase induction levels of metabolic enzymes. Therefore, hPXR plays a critical role in drug metabolism and excretion. Identifying the molecules that activate this protein can be of great help to predict adverse drug interaction, which, nevertheless, cannot be accurately modeled without taking into account its promiscuous nature, namely, highly flexible protein conformation and multiple ligand orientations. An in silico model was developed to predict the activation of hPXR using the novel pharmacophore ensemble/support vector machine (PhE/SVM) scheme. The predictions by the PhE/SVM model are in good agreement with the experimental observations for those molecules in the training set (n = 32, r(2) = 0.86, q(2) = 0.80, RMSE = 0.37, s = 0.21) and test set (n = 120, r(2) = 0.80, RMSE = 0.25, s = 0.19). In addition, this PhE/SVM model performed equally well for those molecules in the outlier set (n = 8, r(2) = 0.91, RMSE = 0.15, s = 0.12) and completely met with those validation criteria generally adopted to gauge the predictivity of a theoretical model. A mock test also verified its predictivity. When compared with crystal structures, the calculated results are consistent with the published hPXR-ligand cocomplex structure and the plasticity nature of hPXR is also revealed. Thus, this accurate, fast, and robust PhE/SVM model can be utilized for predicting the activation of promiscuous hPXR to facilitate drug discovery and development.