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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.


Lovastatin. 2D structure depiction of Lovastatin (PubChem CID 53232).
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Figure 4: Lovastatin. 2D structure depiction of Lovastatin (PubChem CID 53232).

Mentions: In our approach A experiments we first consider the problem of inhibition of the enzyme HMG-CoA reductase (HMGR). Well-known inhibitors of HMGR are chemicals from the drug class of statins (HMG-CoA reductase inhibitors). Most of them are marketed drugs or drugs under development. Inhibition of HMGR lowers the cholesterol levels and prevents cardiovascular diseases [13], which are a major problem in developed countries as coronary artery disease affects 13 to 14 million adults in the United States alone [14]. The statins are structurally quite similar as can be seen in Figures 2, 3, 4, 5, 6 and 7. All of them are competitive inhibitors of HMGR with respect to binding of the substrate HMG-CoA, but not with respect to binding of NADPH [15]. The protein receptor used in the docking procedure is the structure of HMGR co-crystallized with fluvastatin (Figure 8, CID 446155), which is available in the PDB [16] with identifier [PDB:1HWI] [17]. We use two sets of known ligands that are mixed with the decoys and provide the reference compound in this first set of experiments: first the set of statins and second the HMGR ligands provided by the DuD HMGR data set. In case the statins are used as ligand set, we repeat the experiment with each statin as query compound, otherwise we randomly select ten DuD HMGR ligands and use each one of those as query compound.


Improving structural similarity based virtual screening using background knowledge.

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

Lovastatin. 2D structure depiction of Lovastatin (PubChem CID 53232).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Lovastatin. 2D structure depiction of Lovastatin (PubChem CID 53232).
Mentions: In our approach A experiments we first consider the problem of inhibition of the enzyme HMG-CoA reductase (HMGR). Well-known inhibitors of HMGR are chemicals from the drug class of statins (HMG-CoA reductase inhibitors). Most of them are marketed drugs or drugs under development. Inhibition of HMGR lowers the cholesterol levels and prevents cardiovascular diseases [13], which are a major problem in developed countries as coronary artery disease affects 13 to 14 million adults in the United States alone [14]. The statins are structurally quite similar as can be seen in Figures 2, 3, 4, 5, 6 and 7. All of them are competitive inhibitors of HMGR with respect to binding of the substrate HMG-CoA, but not with respect to binding of NADPH [15]. The protein receptor used in the docking procedure is the structure of HMGR co-crystallized with fluvastatin (Figure 8, CID 446155), which is available in the PDB [16] with identifier [PDB:1HWI] [17]. We use two sets of known ligands that are mixed with the decoys and provide the reference compound in this first set of experiments: first the set of statins and second the HMGR ligands provided by the DuD HMGR data set. In case the statins are used as ligand set, we repeat the experiment with each statin as query compound, otherwise we randomly select ten DuD HMGR ligands and use each one of those as query compound.

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.