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Combination Rules for Group Fusion in Similarity-Based Virtual Screening
Beining Chen1, Christoph Mueller1,2, Peter Willett3
1Department of Chemistry, University of Sheffield, Sheffield S10 2TN, UK.
Molecular Informatics
|July 28, 2016
Summary
Group fusion effectively screens compounds by combining multiple similarity searches. Using reciprocal rank positions and maximizing reference structures improves screening performance for drug discovery.
Area of Science:
- Computational chemistry
- Cheminformatics
- Drug discovery
Background:
- Similarity searching is crucial for identifying bioactive molecules.
- Combining results from multiple searches (group fusion) can enhance screening accuracy.
- Evaluating different fusion rules is essential for optimizing this process.
Purpose of the Study:
- To assess the effectiveness of 15 parameter-free rules for group fusion in similarity searching.
- To identify optimal conditions for group fusion using ECFC_4 fingerprints and a Bayesian inference network.
Main Methods:
- Evaluated 15 similarity-based and rank-based group fusion rules.
- Utilized ECFC_4 fingerprints for molecular similarity calculations.
- Employed a Bayesian inference network for combining search outputs.
- Tested on the MDDR and WOMBAT databases.
Main Results:
- Group fusion is most effective when a large number of reference structures are used.
- Submitting only a small proportion of each ranked similarity list to the fusion rule improves performance.
- A fusion rule based on reciprocal rank positions demonstrated superior effectiveness.
Conclusions:
- Group fusion is a powerful technique for enhancing virtual screening.
- Reciprocal rank-based fusion rules are highly effective due to their correlation with activity probability.
- Optimizing the number of reference structures and the proportion of ranked lists is key to successful group fusion.
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