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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

(A) Structural superposition of TK actives after pharmacophore mapping and binding pocket fitting. The conformations of the actives predicted by DOCK (B), GOLD (C), and Glide (D) are also displayed.
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fig7: (A) Structural superposition of TK actives after pharmacophore mapping and binding pocket fitting. The conformations of the actives predicted by DOCK (B), GOLD (C), and Glide (D) are also displayed.

Mentions: To test the above notion, we re-analyzed the virtual screening results by fitting the actives at 5% of the highest ranks of the entire databases to the pharmacophore models and target binding sites, respectively. The results are shown in Figures 6, 7, 8, 9 and Figures S2–S13 in the Supplementary Information. Here, we only use the result against TK and ERα as examples to illustrate why the PBVS enrichments are higher than those of DBVS.


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)

(A) Structural superposition of TK actives after pharmacophore mapping and binding pocket fitting. The conformations of the actives predicted by DOCK (B), GOLD (C), and Glide (D) are also displayed.
© Copyright Policy
Related In: Results  -  Collection

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

fig7: (A) Structural superposition of TK actives after pharmacophore mapping and binding pocket fitting. The conformations of the actives predicted by DOCK (B), GOLD (C), and Glide (D) are also displayed.
Mentions: To test the above notion, we re-analyzed the virtual screening results by fitting the actives at 5% of the highest ranks of the entire databases to the pharmacophore models and target binding sites, respectively. The results are shown in Figures 6, 7, 8, 9 and Figures S2–S13 in the Supplementary Information. Here, we only use the result against TK and ERα as examples to illustrate why the PBVS enrichments are higher than those of DBVS.

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