Correlation of observed and predicted activities (training set in blue and test set in red).

Correlation of observed and predicted activities (training set in blue and test set in red).</p

Graphs representing the Radius of gyration (Rg) values for MT and WT proteases without as with ligands (ND and DRV) during the period of simulation.

Graphs representing the Radius of gyration (Rg) values for MT and WT proteases without as with ligands (ND and DRV) during the period of simulation.</p

2D-binding interactions in the active site of the wild type protease (WT-Darunavir).

2D-binding interactions in the active site of the wild type protease (WT-Darunavir).</p

Negative logarithm values of the biological activity concerning the 33 compounds.

Negative logarithm values of the biological activity concerning the 33 compounds.</p

Graphs showing the number of hydrogen bonds (at every 20 ns) along with the simulation time for complex compounds containing MT and WT proteases.

Graphs showing the number of hydrogen bonds (at every 20 ns) along with the simulation time for complex compounds containing MT and WT proteases.</p

3D structure of complex compound with WT protease (WT-DRV).

3D structure of complex compound with WT protease (WT-DRV).</p

Multi-collinearity statistics.

Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model’s applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski’s rule. Molecular Docking performed on Wild Type, and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes’ stability allowing a comparative study on the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of darunavir and maybe used for further experimental studies. Our study may also be used as pipeline to search and design new potential inhibitors of HIV-1 proteases.</div

3D-binding interactions in the active site of the wild type protease (WT-ND).

3D-binding interactions in the active site of the wild type protease (WT-ND).</p

Descriptors’ computed values and predicted activities as well of the test set compounds using the MLR model generated.

Descriptors’ computed values and predicted activities as well of the test set compounds using the MLR model generated.</p

Flow chart of the current work.

Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model’s applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski’s rule. Molecular Docking performed on Wild Type, and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes’ stability allowing a comparative study on the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of darunavir and maybe used for further experimental studies. Our study may also be used as pipeline to search and design new potential inhibitors of HIV-1 proteases.</div