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Topomer CoMFA and HQSAR Study on Benzimidazole Derivative as NS5B Polymerase Inhibitor
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Background:
In recent years, the number of people infected with the hepatitis C virus
(HCV) is increasing rapidly. This has become a major threat to global health, therefore, new anti-
HCV drugs are urgently needed. HCV NS5B polymerase is an RNA-dependent RNA polymerase
(RdRp), which plays an important role in virus replication, and can effectively prevent the replication
of HCV sub-genomic RNA in daughter cells. It is considered a very promising HCV therapeutic
target for the design of anti-HCV drugs.
Methods:
In order to explore the relationship between the structure of benzimidazole derivative and
its inhibitory activity on NS5B polymerase, holographic quantitative structure-activity relationship
(HQSAR) and Topomer comparative molecular field analysis (CoMFA) were used to establish benzimidazole
QSAR model of derivative inhibitors.
Results:
The results show that for the Topomer CoMFA model, the cross-validation coefficient q2
value is 0.883, and the non-cross-validation coefficient r2 value is 0.975. The model is reasonable,
reliable, and has a good predictive ability. For the HQSAR model, the cross-validated q2 value is
0.922, and the uncross-validated r2 value is 0.971, indicating that the model data fit well and has a
high predictive ability. Through the analysis of the contour map and color code diagram, 40 new
benzimidazole inhibitor molecules were designed, and all of them have higher activity than template
molecules, and the new molecules have significant interaction sites with protein 3SKE.
Conclusion:
The 3D-QSAR model established by Topomer CoMFA and HQSAR has good prediction
results and the statistical verification is valid. The newly designed molecules and docking results
provide theoretical guidance for the synthesis of new NS5B polymerase inhibitors and for the identification
of key residues that the inhibitors bind to NS5B, which helps to better understand their inhibitory
mechanism. These findings are helpful for the development of new anti-HCV drugs.
Title: Topomer CoMFA and HQSAR Study on Benzimidazole Derivative as
NS5B Polymerase Inhibitor
Description:
Background:
In recent years, the number of people infected with the hepatitis C virus
(HCV) is increasing rapidly.
This has become a major threat to global health, therefore, new anti-
HCV drugs are urgently needed.
HCV NS5B polymerase is an RNA-dependent RNA polymerase
(RdRp), which plays an important role in virus replication, and can effectively prevent the replication
of HCV sub-genomic RNA in daughter cells.
It is considered a very promising HCV therapeutic
target for the design of anti-HCV drugs.
Methods:
In order to explore the relationship between the structure of benzimidazole derivative and
its inhibitory activity on NS5B polymerase, holographic quantitative structure-activity relationship
(HQSAR) and Topomer comparative molecular field analysis (CoMFA) were used to establish benzimidazole
QSAR model of derivative inhibitors.
Results:
The results show that for the Topomer CoMFA model, the cross-validation coefficient q2
value is 0.
883, and the non-cross-validation coefficient r2 value is 0.
975.
The model is reasonable,
reliable, and has a good predictive ability.
For the HQSAR model, the cross-validated q2 value is
0.
922, and the uncross-validated r2 value is 0.
971, indicating that the model data fit well and has a
high predictive ability.
Through the analysis of the contour map and color code diagram, 40 new
benzimidazole inhibitor molecules were designed, and all of them have higher activity than template
molecules, and the new molecules have significant interaction sites with protein 3SKE.
Conclusion:
The 3D-QSAR model established by Topomer CoMFA and HQSAR has good prediction
results and the statistical verification is valid.
The newly designed molecules and docking results
provide theoretical guidance for the synthesis of new NS5B polymerase inhibitors and for the identification
of key residues that the inhibitors bind to NS5B, which helps to better understand their inhibitory
mechanism.
These findings are helpful for the development of new anti-HCV drugs.
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