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In order to better understand the biochemical interactions governing their activities in lowering blood pressure, multiple quantitative structure-activity relationship (QSAR) models were developed from a data set of 58 angiotensin converting enzyme (ACE) inhibitors. The models were built by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The best CoMFA model had a cross-validation q2 value of 0.940, a non cross-validation r2 value of 0.952 and an external validation statistic Qext 2 value of 0.920. For the best CoMSIA model the values were with q2 = 0.872, r2 = 0.926 and Qext 2 = 0.868. Based on the contour maps, eight new ACE inhibitors were designed. Molecular docking was employed to further explore the binding requirements between the ligands and the receptor protein which included several hydrogen bonds between the ACE inhibitors and active site residues. This study showed extensive interactions between ACE inhibitors and residues of HIS383, GLU384, HIS513, TYR520 and LYS511 in the active site of ACE. The design of potent new inhibitors of ACE can get useful insights from these results.

The ionic-interactions of acetic acid in H2O–PVOH and H2O–alcohol– PVOH solvent systems were studied at different temperatures by the viscosity method. The viscosities of the poly(vinyl alcohol) (PVOH) were increased with increasing concentration of PVOH and decreased with the increasing concentration of acetic acid. The viscosity data were used to evaluate the ion–ion interactions and ion–solvent interactions in terms of the A and B coefficients of the Jones–Dole equation, respectively. The negative values of the B-coefficient increased with increasing temperature, showing that the acid behaves as a structure breaker in PVOH–solvent mixtures and consequently, the values of the A-coefficient were decreased with increasing temperature. Thermodynamic parameters, such as energy of activation (Ea*), Gibbs energy change of activation (ΔG*) and entropy change of activation (ΔS*) were also calculated as a function of the acid (CH3COOH) concentration, PVOH composition, alcohols and temperature.

In this work, a 3D-QSAR model involving for 40 dipeptidyl boronic acid proteasome inhibitors was built based on Topomer CoMFA. The multiple correlation coefficient of fitting, cross-validation and external validation were 0.908, 0.647 and 0.703, respectively. The results indicated that the obtained Topomer CoMFA model has not only the favourable estimation stability but also the good prediction capability. Topomer Search was employed as a tool for virtual screening in lead-like compounds of ZINC database. Finally, 1 R1 group, 7 R2 groups and 6 R3 groups with higher contribution values were employed to alternately substitute the R1, R2 and R3 of the templete compound 23 with highest bioactivity. As a consequence, 33 new molecules with higher activity than that of the model molecule were designed successfully. The results showed that the Topomer Search technology could be effectively apply to screen and design new dipeptidyl boronic acid proteasome inhibitors and has good predictive capability to design new dipeptidyl boronic acid proteasome inhibitors drugs as guidance.

In this paper, a three-dimensional quantitative structure-activity relationship (3D-QSAR) study for 62 HIV-1 integrase(IN) inhibitors was established using Topomer CoMFA. The multiple correlation coefficient of fitting, cross validation and external validation were 0.942, 0.670 and 0.748, respectively. The results indicated that the Topomer CoMFA model obtained has both favorable estimation stability and good prediction capability. Topomer Search was used to search R group from ZINC database. As the result, a series of R groups with relatively high activity contribution was obtained. By filtering with the most potent molecule in the set, 1 Ra group and 21 Rb groups were selected. We employed the 1 Ra groups and 21 Rb groups to alternately substitute the Ra and Rb of sample 42. Finally, we designed 21 new compounds and further predicted their activities using the Topomer CoMFA model and there were 10 new compounds with higher activity than that of the template molecule. The results suggested the Topomer Search technology could be effectively used to screen and design new HIV-1 IN inhibitors and has good predictive capability to guide the design of new HIV/AIDS drugs.

Quantitative structure-activity relationships (QSAR) approach is used for finding the relationship between molecular structures and the activity of peptide drugs. In this work, stepwise multiple regression, was employed to select optimal subset of descriptors that have significant contribution to the drug activity of 21 oxytocin analogues, 48 bitter tasting threshold, and 58 angiotensin-converting enzyme inhibitors. A new set of descriptor, SVWGM, was used for the prediction of the drug activity of peptide drugs and then were used to build the model by partial least squares method, for it’s estimation stability and generalization ability was strictly analyzed by both internal and external validations, with cross-validation correlation coefficient, correlation coefficient and correlation coefficient of external validation.