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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.


Plot ofα vs. MeanΔEF for MCS ext. On the x-axis the values of the combining factor α is plottet versus the mean ΔEF for MCS ext on the y-axis. (approach B2).
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Figure 19: Plot ofα vs. MeanΔEF for MCS ext. On the x-axis the values of the combining factor α is plottet versus the mean ΔEF for MCS ext on the y-axis. (approach B2).

Mentions: While the results already show improvements of the score for a fixed α of 0.3, one might be interested in the results for an optimal α, which we do not know beforehand. Also, it is interesting to know into which range optimal αs fall and whether 0.3 is a suitable default value. Results are shown in Tables 14, 15 and 16 as well as in Figures 19 and 20. As it turns out, the statistics of the number of wins and losses can still be improved, e.g., from 8:2, 7:3, 8:2 to 10:0, 9:0, 9:1, respectively, and so forth. On the other hand, the optimal αs seem to vary somewhat, with a value of 0.3 not being too large for most data sets and most percentages of retrieved compounds (see Table 14).


Improving structural similarity based virtual screening using background knowledge.

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

Plot ofα vs. MeanΔEF for MCS ext. On the x-axis the values of the combining factor α is plottet versus the mean ΔEF for MCS ext on the y-axis. (approach B2).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 19: Plot ofα vs. MeanΔEF for MCS ext. On the x-axis the values of the combining factor α is plottet versus the mean ΔEF for MCS ext on the y-axis. (approach B2).
Mentions: While the results already show improvements of the score for a fixed α of 0.3, one might be interested in the results for an optimal α, which we do not know beforehand. Also, it is interesting to know into which range optimal αs fall and whether 0.3 is a suitable default value. Results are shown in Tables 14, 15 and 16 as well as in Figures 19 and 20. As it turns out, the statistics of the number of wins and losses can still be improved, e.g., from 8:2, 7:3, 8:2 to 10:0, 9:0, 9:1, respectively, and so forth. On the other hand, the optimal αs seem to vary somewhat, with a value of 0.3 not being too large for most data sets and most percentages of retrieved compounds (see Table 14).

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.