Wednesday, October 21, 2009
Combining docking with pharmacophore filtering for improved virtual screening
Author: Megan L Peach and Marc C Nicklaus
Source: Journal of Cheminformatics 2009, 1:6 doi:10.1186/1758-2946-1-6
The main idea of this article is to evaluate "pharmacophore filtering" that is used as a post processing docking (ranking molecules) method in virtual screening.
One of the main issues in virtual screening is that it still gives too many false positives. Virtual screening aims to select active molecules from large database but predicted active molecules are often not active in real experiment. According to the article, there are two theories of the lack of docking based virtual screening. One is that docking program is not accurate enough to generate correct binding poses and the other is that the scoring functions don't succeed to rank molecules obtained from docking.
The authors evaluated "pharmacophore filtering" method and compared it to other scoring functions in terms of post processing. Firstly, they performed docking by Gold and Glide and combined all the obtained poses into one structure file. Secondly, the performed post processing for both scoring function and "pharmacophore filtering. They determined pharmacophore models for "pharmacophore filtering" in MOE. GScore in Glide and GoldScore and Chemscore in Gold, and Affinity score are used as scoring functions. Three different target proteins were used for evaluation, such as Neuraminidase A, CDK2, and PKC C1 domain which have various binding characteristics. The authors also tested the performance of "pharmacophore filtering" method itself with different pharmacophore models using active molecules and decoys.
In results, the comparison of "pharmacophore filtering" with scoring function, "pharmacophore filtering" method gave better results with all three target proteins. In addition, the authors discussed visual inspection and intervention for filtering is essential in docking and virtual screening.
In conclusion, "pharmacophore filtering" method improved on discriminating active molecules from large database. The authors also suggested that they used docking as a conformation generator in place of pre-generated conformer database used in general pharmacophore based virtual screening. In addition, pharmacophore filtering which is used as a post-processing has an advantage to reduce complexity of docking process since it enables to bypass several known difficulties of scoring function.
Source: Journal of Cheminformatics 2009, 1:6 doi:10.1186/1758-2946-1-6
The main idea of this article is to evaluate "pharmacophore filtering" that is used as a post processing docking (ranking molecules) method in virtual screening.
One of the main issues in virtual screening is that it still gives too many false positives. Virtual screening aims to select active molecules from large database but predicted active molecules are often not active in real experiment. According to the article, there are two theories of the lack of docking based virtual screening. One is that docking program is not accurate enough to generate correct binding poses and the other is that the scoring functions don't succeed to rank molecules obtained from docking.
The authors evaluated "pharmacophore filtering" method and compared it to other scoring functions in terms of post processing. Firstly, they performed docking by Gold and Glide and combined all the obtained poses into one structure file. Secondly, the performed post processing for both scoring function and "pharmacophore filtering. They determined pharmacophore models for "pharmacophore filtering" in MOE. GScore in Glide and GoldScore and Chemscore in Gold, and Affinity score are used as scoring functions. Three different target proteins were used for evaluation, such as Neuraminidase A, CDK2, and PKC C1 domain which have various binding characteristics. The authors also tested the performance of "pharmacophore filtering" method itself with different pharmacophore models using active molecules and decoys.
In results, the comparison of "pharmacophore filtering" with scoring function, "pharmacophore filtering" method gave better results with all three target proteins. In addition, the authors discussed visual inspection and intervention for filtering is essential in docking and virtual screening.
In conclusion, "pharmacophore filtering" method improved on discriminating active molecules from large database. The authors also suggested that they used docking as a conformation generator in place of pre-generated conformer database used in general pharmacophore based virtual screening. In addition, pharmacophore filtering which is used as a post-processing has an advantage to reduce complexity of docking process since it enables to bypass several known difficulties of scoring function.
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