Quantitative Structure-activity Relationship Pharmacophore Model Research Articles

Theoretical support for the enhancement of infrared spectrum signals by derivatization of phthalic acid esters using a pharmacophore model

ABSTRACTThree-dimensional quantitative structure–activity relationship pharmacophore models of phthalic acid ester (PAE) infrared characteristic vibration spectrum signals were constructed. Then, three PAEs were substituted with hydrophobic groups using the optimal pharmacophore model. The results showed that the pharmacophore model (Hypo 1) was significant and that 16 PAE derivatives were generated. The infrared characteristic vibration spectrum signals of three derivatives were increased by 76.98, 26.49, and 16.42%. The substitution reaction orders were determined, and the enhancement mechanisms of PAEs were analyzed. The findings provide theoretical support for the development of technology to enhance infrared spectrum signals.

Prediction of stability for polychlorinated biphenyls in transformer insulation oil through three-dimensional quantitative structure-activity relationship pharmacophore model and full factor experimental design

Based on the obtained data of half-lives(t1/2) for 31 polychlorinated biphenyl congeners(PCBs), 3D quantitative structure-activity relationship(QSAR) pharmacophore was used to establish a 3D QSAR model to predict the t1/2 values of the remaining 178 PCBs, using the structural parameters as independent variables and lgt1/2 values as the dependent variable. Among this process, the whole data set(31 compounds) was divided into a training set(24 compounds) for model generation and a test set(7 compounds) for model validation. Then, the full factor experimental design was used to research the potential second-order interactional effect between different substituent positions, obtaining the final regulation scheme for PCB. At last, a 3D QSAR pharmacophore model was established to validate the reasonable regulation targeting typical PCB with respect to half-lives and thermostability. As a result, the cross-validation correlation coefficient(q2) obtained by the 3D QSAR model was 0.845(>0.5) and the coefficient of determination(r2) obtained was 0.936(>0.9), indicating that the models were robust and predictive. CoMSIA analyses upon steric, electrostatic and hydrophobic fields were 0.7%, 85.9%, and 13.4%, respectively. The electrostatic field was determined to be a primary factor governing the t1/2. From CoMSIA contour maps, t1/2 increased when substituents possessed electropositive groups at the 2′-, 3-, 3′-, 5- and 5′- positions and electronegative groups at the 3-, 3′-, 5-, 6- and 6′- positions, which could increase the PCB stability in transformer insulation oil. Modification of two typical PCB congeners(PCB-77 and PCB-81) showed that the lgt1/2for three selected modified compounds increased by 13%(average ratio) compared with that of each congener and the thermostability of them were higher, validating the reasonability of the regulatory scheme obtained from the 3D QSAR model. These results are expected to be beneficial in predicting t1/2 values of PCB homologues and derivatives and in providing a theoretical foundation for further elucidation of the stability of PCBs.

An in silico protocol for identifying mTOR inhibitors from natural products

The mammalian target of rapamycin (mTOR) is an anti-cancer target. In this study, we propose an in silico protocol for identifying mTOR inhibitors from the ZINC natural product database. First, a three-dimensional quantitative structure-activity relationship pharmacophore model was built based on known mTOR inhibitors. The model was validated with an external test set, Fischer's randomization method, a decoy set and pharmacophore mapping conformation testing. The results showed that the model can predict the mTOR inhibition activity of the tested compounds. Virtual screening was performed based on the best pharmacophore model, and the results were then filtered using a molecular docking approach. In addition, molecular mechanics/generalized born surface area analysis was used to refine the selected candidates. The top 20 natural products were selected as potential mTOR inhibitors, and their structural scaffolds could serve as building blocks in designing drug-like molecules for mTOR inhibition.