Combination Rules for Group Fusion in Similarity‐Based Virtual Screening

Chen, Beining; Mueller, Christoph; Willett, Peter

doi:10.1002/minf.201000050

Cited by 46 publications

(52 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Any further attempts advocating rational design of ''improved'' similarity-based virtual screening tools should explicitly address the challenge of proving that the method is more likely to succeed on the average over multiple queries [40]. This goal should yet be pursued, in the light of encouraging results from the ''unachieved learning'' approach, and also considering a reinforcement of the capture of topological information.…”

Section: Discussionmentioning

confidence: 98%

Local neighborhood behavior in a combinatorial library context

Horvath

Koch²,

Schneider³

et al. 2011

J Comput Aided Mol Des

View full text Add to dashboard Cite

This article revisits a particular aspect of the molecular similarity principle-the Neighborhood Behavior (NB) concept. Earlier, the NB optimality criterion was introduced to select descriptor spaces, combining a given descriptor set and a similarity metric, which optimally comply with the similarity principle. Here, we focus on a "local" analysis based on the neighborhood of individual bioactive compounds. The defined NB-score measures similarity-based virtual screening success when using individual actives as queries. Systematic studies of local NB have been performed on a large combinatorial library of compounds with reported IC (50) values for five proteases, involving more than 140 descriptor/metric combinations of various fragment- and pharmacophore-based descriptors and different similarity metrics. Although, for each descriptor/metrics combination, the NB-score heavily depends on the query compound, on the average, 2D pharmacophore-based descriptors outperformed their 3D counterparts.

show abstract

Section: Discussionmentioning

confidence: 98%

Local neighborhood behavior in a combinatorial library context

Horvath

Koch²,

Schneider³

et al. 2011

J Comput Aided Mol Des

View full text Add to dashboard Cite

show abstract

“…This rule was proposed for virtual screening purposes [22], by adding a cut-off in the fraction of elements considered for each ranking criterion (e.g. 1%).…”

Section: Reciprocal Rank Fusion Rule (Rrf)mentioning

confidence: 99%

Weighted power–weakness ratio for multi-criteria decision making

Todeschini

Grisoni

Nembri

2015

Chemometrics and Intelligent Laboratory Systems

View full text Add to dashboard Cite

“…Fusion involves a fusion rule that determines how the result set produced by the NN is being combined. There are a number of fusion rules, as described in [11], one of which is the MAX fusion rule. This rule combines the result sets produced by taking the maximum value of similarity score for a particular structure.…”

Section: (3)mentioning

confidence: 99%

The Effect of Adding Indirect Relationship to Turbo Similarity Searching

Malim¹,

Pei-Chia²,

Al-Laila³

et al. 2016

PHARMSCI

View full text Add to dashboard Cite

Background:Turbo Similarity Searching (TSS) has been proved as one of the effective and simple searching method in Cheminformatics. Emerging from the conventional similarity searching, TSS depended on the concept of fusion where relationship between the target being sought and the compound in the database are indirect. Previous works has looked at only one level of indirect relationship and indicates that there are further potential that more levels of such relationship be added to TSS to increase its ability to recover more actives. Hence, in this work, we aimed to investigate the impact of the indirect relationship on TSS. Method:This study has further investigated the enhancement of TSS using additional layers of indirect relationship and fusion process. We implemented TSS by adding another layer of fusion between the target and database compound. Results:The experiments with MDDR database showed that the proposed new strategy described in this paper provide a way of enhancing the effectiveness of the TSS process in chemical databases. The experiments also showed that the increases in performance are particularly better when the sought actives are structurally diverse. Conclusion:We may conclude that the additional layers do increase the recall of TSS. Hence, the new TSS strategy could be used as an alternative to the old TSS.

show abstract

Combination Rules for Group Fusion in Similarity‐Based Virtual Screening

Cited by 46 publications

References 41 publications

Local neighborhood behavior in a combinatorial library context

Local neighborhood behavior in a combinatorial library context

Weighted power–weakness ratio for multi-criteria decision making

The Effect of Adding Indirect Relationship to Turbo Similarity Searching

Contact Info

Product

Resources

About