Computational study of histamine H3-receptor antagonist with support vector machines and three dimension quantitative structure activity relationship methods

QSAR Modeling of Histamine H3R Antagonists/inverse Agonists as Future Drugs for Neurodegenerative Diseases

Background

Histamine H₃ receptor (H₃R) is associated with several neuropsychological diseases, and thus it is an important target involved in several CNS disorders, such as narcolepsy, attention deficit hyperactivity disorder and schizophrenia. Since QSAR modeling is a feasible approach to explain the role of the molecular substituents in the biological activity, it can help in improving the design of better H₃R ligands for these conditions.

Methods

This article reviews papers previously published in literature to show the current status of the contribution from QSAR modeling to reach H₃R antagonists/inverse agonists.

Results

Classical and 3D-QSAR models were retrieved, showing that the steric and hydrophobic properties of the H₃R ligands are most important to reach good affinity.

Conclusion

Although QSAR methods are valuable to design better H₃R antagonists/inverse agonists, pharmacokinetics should also be considered in future models to ensure good CNS penetration.

Computational study of estrogen receptor-alpha antagonist with three-dimensional quantitative structure-activity relationship, support vector regression, and linear regression methods

Human estrogen receptor (ER) isoforms, ERα and ERβ, have long been an important focus in the field of biology. To better understand the structural features associated with the binding of ERα ligands to ERα and modulate their function, several QSAR models, including CoMFA, CoMSIA, SVR, and LR methods, have been employed to predict the inhibitory activity of 68 raloxifene derivatives. In the SVR and LR modeling, 11 descriptors were selected through feature ranking and sequential feature addition/deletion to generate equations to predict the inhibitory activity toward ERα. Among four descriptors that constantly appear in various generated equations, two agree with CoMFA and CoMSIA steric fields and another two can be correlated to a calculated electrostatic potential of ERα.

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.