Insight into the Metabolism Rate of Quinone Analogues from Molecular Dynamics Simulation and 3D-QSMR Methods

Revealing interaction mode between HIV-1 protease and mannitol analog inhibitor

HIV protease is a key enzyme to play a key role in the HIV-1 replication cycle and control the maturation from HIV viruses to an infectious virion. HIV-1 protease has become an important target for anti-HIV-1 drug development. Here, we used molecular dynamics simulation to study the binding mode between mannitol derivatives and HIV-1 protease. The results suggest that the most active compound (M35) has more stable hydrogen bonds and stable native contacts than the less active one (M17). These mannitol derivatives might have similar interaction mode with HIV-1 protease. Then, 3D-QSAR was used to construct quantitative structure-activity models. The cross-validated q(2) values are found as 0.728 and 0.611 for CoMFA and CoMSIA, respectively. And the non-cross-validated r(2) values are 0.973 and 0.950. Nine test set compounds validate the model. The results show that this model possesses better prediction ability than the previous work. This model can be used to design new chemical entities and make quantitative prediction of the bioactivities for HIV-1 protease inhibitors before resorting to in vitro and in vivo experiment.

Computational study of CCR5 antagonist with support vector machines and three dimensional quantitative structure activity relationship methods

CCR5 is the key receptor of HIV-1 virus entry into host cells and it becomes an attractive target for antiretroviral drug design. To date, six types of CCR5 antagonist were synthesized and evaluated. To search more potent bio-active compounds, non-linear support vector machine was used to construct the relationship models for 103 oximino-piperidino-piperidine CCR5 antagonists. Then, comparative molecular field analysis and comparative molecular similarity indices analysis models were constructed after alignment with their common substructure. Twenty-one structural diverse compounds, which were not included in the support vector machine, comparative molecular field analysis, and comparative molecular similarity indices analysis models, validated these models. The results show that these models possess good predictive ability. When comparing between support vector machine and 3D-quantitative structure activity relationship models, the results obtained from these two methods are compatible. However, 3D-quantitative structure activity relationship model is significantly better than support vector machine model and previous reported pharmacophore model. These models can help us to make quantitative prediction of their bio-activities before in vitro and in vivo stages.

In silico log P prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression

Oil/water partition coefficient (log P) is one of the key points for lead compound to be drug. In silico log P models based solely on chemical structures have become an important part of modern drug discovery. Here, we report support vector machines, radial basis function neural networks, and multiple linear regression methods to investigate the correlation between partition coefficient and physico-chemical descriptors for a large data set of compounds. The correlation coefficient r(2) between experimental and predicted log P for training and test sets by support vector machines, radial basis function neural networks, and multiple linear regression is 0.92, 0.90, and 0.88, respectively. The results show that non-linear support vector machines derives statistical models that have better prediction ability than those of radial basis function neural networks and multiple linear regression methods. This indicates that support vector machines can be used as an alternative modeling tool for quantitative structure-property/activity relationships studies.

Computational study of the binding mode of epidermal growth factor receptor kinase inhibitors

Epidermal growth factor receptor kinase is relative to the progression of various types of cancers. In order to design anticancer drug, docking and support vector machines were used to guide CoMFA and CoMSIA for constructing optimal 3D-QSAR model. Additional descriptors, log P and HOMO, combined with several fields of CoMFA and CoMSIA, were introduced to construct models for the inhibitor of epidermal growth factor receptor kinase. The results show that the inclusion of log P and HOMO energy is meaningful for 3D-QSAR model. The validation of these models was testified by some structurally diverse compounds, which were not included in the CoMFA and CoMSIA models. The docking study and molecular dynamic simulation permit us to insight into the binding mode between ligand and EGFR kinase, and provide important information for structure-based drug design. The proposed approach can also be extended to other QSAR investigations.