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Turbo Similarity Searching: Effect of Partial Ranking and Fusion Rules on ChEMBL Database.
Abeer Abdulhakeem Mansour Alhasbary1, Nurul Hashimah Ahamed Hassain Malim1
1School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia.
Molecular Informatics
|December 8, 2021
Summary
Turbo Similarity Searching (TSS) performance improves with partial ranking, decreasing database proportions, and fewer nearest neighbours (NNs). Fusion rules like reciprocal rank position (RKP) were found to be superior.
Area of Science:
- Computational chemistry
- Cheminformatics
- Drug discovery
Background:
- Turbo Similarity Searching (TSS) is an advanced chemical similarity searching (SS) method.
- TSS effectiveness is influenced by structural representation, similarity coefficient, number of nearest neighbours (NNs), and fusion rule.
- Prior research indicated benefits of using extensive reference compounds with limited database fractions for improved group fusion recall.
Purpose of the Study:
- To investigate the impact of partial ranking on TSS using various fusion rules and NNs.
- To assess if previous findings on database fractions hold true for TSS.
- To examine the influence of TSS's indirect relationship feature on partial ranking.
Main Methods:
- Utilized the ChEMBL database for analysis.
- Implemented partial ranking strategies within TSS.
- Varied the number of nearest neighbours (NNs) and employed different fusion rules.
- Evaluated the effect of database proportion on fusion outcomes.
Main Results:
- Partial ranking significantly impacted TSS results.
- TSS performance enhanced with decreased database proportions and a smaller number of NNs.
- Fusion rules based on reciprocal rank position (RKP), maximum similarity score (sMAX), and sMNZ outperformed others.
Conclusions:
- Partial ranking is a significant factor in optimizing TSS.
- Reducing database fractions and NNs improves TSS efficiency.
- RKP, sMAX, and sMNZ fusion rules offer superior performance in TSS.
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