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Improving structural similarity based virtual screening using background knowledge.

Girschick T, Puchbauer L, Kramer S - J Cheminform (2013)

Bottom Line: The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings.Our study shows that adding binding relevant background knowledge can lead to significantly improved similarity rankings in virtual screening and that even basic data mining approaches can lead to competitive results making hand-selection of the background knowledge less crucial.This is especially important in drug discovery and development projects where no receptor structure is available or more frequently no verified binding mode is known and mostly ligand based approaches can be applied to generate hit compounds.

View Article: PubMed Central - HTML - PubMed

Affiliation: Johannes Gutenberg-Universität Mainz, Institut für Informatik, Staudingerweg 9, 55128 Mainz, Germany. kramer@informatik.uni-mainz.de.

ABSTRACT

Background: Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient performance in some application cases. In this paper, we provide a link between ranking-based virtual screening and fragment-based data mining methods. The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings. This background knowledge in the form of binding relevant substructures can either be derived by hand selection or by automated fragment-based data mining methods.

Results: In virtual screening experiments we show that our approach clearly improves enrichment factors with both applied variants of our approach: the extension of the structural similarity measure with background knowledge in the form of a hand-selected relevant substructure or the extension of the similarity measure with background knowledge derived with data mining methods.

Conclusion: Our study shows that adding binding relevant background knowledge can lead to significantly improved similarity rankings in virtual screening and that even basic data mining approaches can lead to competitive results making hand-selection of the background knowledge less crucial. This is especially important in drug discovery and development projects where no receptor structure is available or more frequently no verified binding mode is known and mostly ligand based approaches can be applied to generate hit compounds.

No MeSH data available.


ZINC1112466. 2D structure depiction of ZINC1112466 from the extended MCS similarity ranking (MCS ext). Rank difference: ΔRank=2.
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Figure 15: ZINC1112466. 2D structure depiction of ZINC1112466 from the extended MCS similarity ranking (MCS ext). Rank difference: ΔRank=2.

Mentions: We then calculated a similarity ranking with the extended MCS similarity measure and again docked the top 25 compounds. The results of docking the top 25 compounds of the extended MCS similarity ranking are shown in Table 5 (see Additional file 1: Table S2 of the supplement). Four structures from the ranking are shown in Figures 14, 15, 16 and 17. The docking scores are clearly improved in comparison to those of the structures found by the MCS similarity ranking given in Table 4 (see Additional file 1: Table S1). This means that the compounds found will very likely have a higher binding affinity to the receptor. Figures 8, 9 and 10 show the original position of fluvastatin and dockings of the two non-statins with the best docking score from the two similarity rankings. It can be seen that the ligand of the extended MCS similarity (in Figure 10) enters the active site much better than the one of the MCS similarity (in Figure 9).


Improving structural similarity based virtual screening using background knowledge.

Girschick T, Puchbauer L, Kramer S - J Cheminform (2013)

ZINC1112466. 2D structure depiction of ZINC1112466 from the extended MCS similarity ranking (MCS ext). Rank difference: ΔRank=2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3928642&req=5

Figure 15: ZINC1112466. 2D structure depiction of ZINC1112466 from the extended MCS similarity ranking (MCS ext). Rank difference: ΔRank=2.
Mentions: We then calculated a similarity ranking with the extended MCS similarity measure and again docked the top 25 compounds. The results of docking the top 25 compounds of the extended MCS similarity ranking are shown in Table 5 (see Additional file 1: Table S2 of the supplement). Four structures from the ranking are shown in Figures 14, 15, 16 and 17. The docking scores are clearly improved in comparison to those of the structures found by the MCS similarity ranking given in Table 4 (see Additional file 1: Table S1). This means that the compounds found will very likely have a higher binding affinity to the receptor. Figures 8, 9 and 10 show the original position of fluvastatin and dockings of the two non-statins with the best docking score from the two similarity rankings. It can be seen that the ligand of the extended MCS similarity (in Figure 10) enters the active site much better than the one of the MCS similarity (in Figure 9).

Bottom Line: The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings.Our study shows that adding binding relevant background knowledge can lead to significantly improved similarity rankings in virtual screening and that even basic data mining approaches can lead to competitive results making hand-selection of the background knowledge less crucial.This is especially important in drug discovery and development projects where no receptor structure is available or more frequently no verified binding mode is known and mostly ligand based approaches can be applied to generate hit compounds.

View Article: PubMed Central - HTML - PubMed

Affiliation: Johannes Gutenberg-Universität Mainz, Institut für Informatik, Staudingerweg 9, 55128 Mainz, Germany. kramer@informatik.uni-mainz.de.

ABSTRACT

Background: Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient performance in some application cases. In this paper, we provide a link between ranking-based virtual screening and fragment-based data mining methods. The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings. This background knowledge in the form of binding relevant substructures can either be derived by hand selection or by automated fragment-based data mining methods.

Results: In virtual screening experiments we show that our approach clearly improves enrichment factors with both applied variants of our approach: the extension of the structural similarity measure with background knowledge in the form of a hand-selected relevant substructure or the extension of the similarity measure with background knowledge derived with data mining methods.

Conclusion: Our study shows that adding binding relevant background knowledge can lead to significantly improved similarity rankings in virtual screening and that even basic data mining approaches can lead to competitive results making hand-selection of the background knowledge less crucial. This is especially important in drug discovery and development projects where no receptor structure is available or more frequently no verified binding mode is known and mostly ligand based approaches can be applied to generate hit compounds.

No MeSH data available.