Molecular modeling studies of human immunodeficiency virus type 1 protease inhibitors using three‐dimensional quantitative structure‐activity relationship, virtual screening, and docking simulations

Abstract

In this paper, two 3‐dimensional quantitative structure‐activity relationship models for 60 human immunodeficiency virus (HIV)‐1 protease inhibitors were established using random sampling analysis on molecular surface and translocation comparative molecular field vector analysis (Topomer CoMFA). The non–cross‐validation (r2), cross‐validation (q2), correlation coefficient of external validation (Q2ext), and F of 2 models were 0.94, 0.80, 0.79, and 198.84 and 0.94, 0.72, 0.75, and 208.53, respectively. The results indicated that 2 models were reasonable and had good prediction ability. Topomer Search was used to search R groups in the ZINC database, 20 new compounds were designed, and the Topomer CoMFA model was used to predicate the biological activity. The results showed that 18 new compounds were more active than the template molecule. So the Topomer Search is effective in screening and can guide the design of new HIV/AIDS drugs. The mechanism of action was studied by molecular docking, and it showed that the protease inhibitors and Ile50, Asp25, and Arg8 sites of HIV‐1 protease have interactions. These results have provided an insight for the design of new potent inhibitors of HIV‐1 protease.

Molecular modeling studies of HIV-1 non-nucleoside reverse transcriptase inhibitors using 3D-QSAR, virtual screening, and docking simulations

“…When q 2 is larger than 0.5 and r 2 is greater than 0.6, it is generally believed that the model has statistical significance. 33 The biological activity predicted by the Topomer CoMFA model for all the molecules are shown in Table III. The linear regression between the experimental pIC 50 and the predicted pIC 50 , for both the training set and the test set is shown in Fig.…”

Drug Design, Molecular Docking and Molecular Dynamics Simulations of Indole Class HIV‐1 NNRTIs Explored with QSAR and Topomer Search

“…The predictive correlation coefficient values ( R 2 pred ), r 2 , k , k’ , r 0 2 , r 0 ’ 2 , r m 2 , r m ’2 , Δ r m 2 , $${\overline {r_m^2 } }$$ are the essential parameter to prove how capable the model is of making positive external predictions. And the ideal model needs to meet R 2 pre d greater than 6, both the value of r m 2 and r m ’2 are higher than 0.5, k and k’ are in the range of 0.85 and 1.15, Δ r m 2 is lower 0.2, $${\overline {r_m^2 } }$$ larger than 0.5 [27–29] . Furthermore, the most significant external prediction parameters R 2 pred of the model is calculated as follows: [20,30] $$$\vcenter{\openup.5em\halign{$\displaystyle{#}$\cr r_{pred}^2 = {{\left( {SD - PRESS} \right)} \over {SD}}\hfill\cr}}$$$ …”

“…And the ideal model needs to meet R 2 pred greater than 6, both the value of r m 2 and r m '2 are higher than 0.5, k and k' are in the range of 0.85 and 1.15, Δr m 2 is lower 0.2, r 2 m larger than 0.5. [27][28][29] Furthermore, the most significant external prediction parameters R 2 pred of the model is calculated as follows: [20,30]…”

The design of compounds with desirable properties – The anti‐HIV case study

“…For example, Zakariazadeh et al 12 designed four different QSAR models combining quantum and molecular mechanical descriptors for naphthyridine derivatives against HIV‐1 integrase (HIV‐IN) activity. Tong et al 26 build 3D‐QSAR models using random sampling analysis on molecular surface and translocation comparative molecular field vector analysis. They designed 18 new compounds, whose activity against HIV‐1 protease (HIV‐PR) were predicted to be higher than the activity of the template molecule, and explored the mechanism of action by molecular docking.…”