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Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets.

Chen Z, Li HL, Zhang QJ, Bao XG, Yu KQ, Luo XM, Zhu WL, Jiang HL - Acta Pharmacol. Sin. (2009)

Bottom Line: Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes.The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS.The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.

View Article: PubMed Central - PubMed

Affiliation: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.

ABSTRACT

Aim: This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods.

Methods: All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors alpha (ERalpha), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS.

Results: Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS.

Conclusion: The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.

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Related in: MedlinePlus

Comparison of hit rates retrieved using DOCK (red), GOLD (green), and Glide (blue) at 2% and 5% of the highest ranks of the entire databases (databases I and II) with different crystal structures for the target of TK. The last columns (Aver) represent the hit rate over the employed crystal structures for the three docking programs and the pharmacophore-based method (cyan).
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fig12: Comparison of hit rates retrieved using DOCK (red), GOLD (green), and Glide (blue) at 2% and 5% of the highest ranks of the entire databases (databases I and II) with different crystal structures for the target of TK. The last columns (Aver) represent the hit rate over the employed crystal structures for the three docking programs and the pharmacophore-based method (cyan).

Mentions: In the TK case, we also performed virtual screening against 21 crystal structures of TK. Glide outperformed other docking-based methods for this target (Figure 12). At the top 2%, the hit rates yielded by Glide against 1E2K, 1E2P, 1KI3, and 1VTK were the same as that of the pharmacophore-based method for database I (Figure 12A), while the hit rates against 1E2I and 1KI7 were higher than that of the pharmacophore-based method (Figure 12A). For other crystal structures, the screening accuracy of the docking-based method was not found to be better than that of the pharmacophore-based method. For database II, the hit rates of the three docking programs were also lower than that of the pharmacophore-based method (Figure 12B and 12D), both at the top 2% and 5% level.


Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets.

Chen Z, Li HL, Zhang QJ, Bao XG, Yu KQ, Luo XM, Zhu WL, Jiang HL - Acta Pharmacol. Sin. (2009)

Comparison of hit rates retrieved using DOCK (red), GOLD (green), and Glide (blue) at 2% and 5% of the highest ranks of the entire databases (databases I and II) with different crystal structures for the target of TK. The last columns (Aver) represent the hit rate over the employed crystal structures for the three docking programs and the pharmacophore-based method (cyan).
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4007494&req=5

fig12: Comparison of hit rates retrieved using DOCK (red), GOLD (green), and Glide (blue) at 2% and 5% of the highest ranks of the entire databases (databases I and II) with different crystal structures for the target of TK. The last columns (Aver) represent the hit rate over the employed crystal structures for the three docking programs and the pharmacophore-based method (cyan).
Mentions: In the TK case, we also performed virtual screening against 21 crystal structures of TK. Glide outperformed other docking-based methods for this target (Figure 12). At the top 2%, the hit rates yielded by Glide against 1E2K, 1E2P, 1KI3, and 1VTK were the same as that of the pharmacophore-based method for database I (Figure 12A), while the hit rates against 1E2I and 1KI7 were higher than that of the pharmacophore-based method (Figure 12A). For other crystal structures, the screening accuracy of the docking-based method was not found to be better than that of the pharmacophore-based method. For database II, the hit rates of the three docking programs were also lower than that of the pharmacophore-based method (Figure 12B and 12D), both at the top 2% and 5% level.

Bottom Line: Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes.The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS.The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.

View Article: PubMed Central - PubMed

Affiliation: Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.

ABSTRACT

Aim: This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods.

Methods: All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors alpha (ERalpha), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS.

Results: Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS.

Conclusion: The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.

Show MeSH
Related in: MedlinePlus