Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins.

Schuffenhauer, Ansgar; Acklin, Pierre; Jacoby, Edgar

doi:10.1002/chin.200321200

Cited by 82 publications

(157 citation statements)

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“…Other groups used Bayesian classifiers [46,57,58]. More specialized chemical similarity methods have also been developed that take, for example, the similarity of target proteins into account [59]. Homology relations between proteins can be exploited to predict binding of drugs to proteins that are related to known drug targets [2].…”

Section: Concepts For Large-scale Drug-target Predictionsmentioning

confidence: 99%

Large‐scale prediction of drug–target relationships

et al. 2008

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The rapidly increasing amount of publicly available knowledge in biology and chemistry enables scientists to revisit many open problems by the systematic integration and analysis of heterogeneous novel data. The integration of relevant data does not only allow analyses at the network level, but also provides a more global view on drug-target relations. Here we review recent attempts to apply large-scale computational analyses to predict novel interactions of drugs and targets from molecular and cellular features. In this context, we quantify the family-dependent probability of two proteins to bind the same ligand as function of their sequence similarity. We finally discuss how phenotypic data could help to expand our understanding of the complex mechanisms of drug action.

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Section: Concepts For Large-scale Drug-target Predictionsmentioning

confidence: 99%

Large‐scale prediction of drug–target relationships

et al. 2008

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“…Grouping by target family is also another method helping focus on particular target-directed privileged structures [40]. The idea is that structurally similar target family members will bind structurally similar small molecule ligands [41]. NMR screening helps identify privileged protein binding elements albeit of smaller size [42].…”

Section: Positive Desirable Chemistry Filtersmentioning

confidence: 99%

Capter 11 Filtering in Drug Discovery

Lipinski

2005

Annual Reports in Computational Chemistry

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“…The best performance was shown by Unity 2D-fingerprints in combination with 1-NN method (which is equivalent to the MAX group fusion). The conclusion being made is that homology-based similarity searching is proven to be effective for identifying ligands of targets without the availability of known ligands which makes it different from previous virtual screening approaches that require at least one known ligand [9].…”

Section: Introductionmentioning

confidence: 98%

“…Schuffenhauer and co-worker [9] introduced homology-based similarity searching which is based on the "structure-activity relationship homology" concept, which is analogous to the Similar Property Principle. Activity homologues (resulted from previous screening) to a new target as reference compounds set for similarity searching are used in their study.…”

Section: Introductionmentioning

confidence: 99%

New strategy for Turbo Similarity Searching: Implementation and testing

Malim

Pei-Chia

Arif

2013

2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS)

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Virtual screening is one of the most vital methods applied in Chemoinformatics, the field that contributes to drug discovery process. Turbo Similarity Searching (TSS) and data fusion are two of the latest chemical similarity searching strategies, which has evolved from the conventional similarity searching (SS) that apply the concept of multi-target searching instead of just an individual target search. The indirect relationship exists in TSS, with the inclusion of Nearest Neighbours (NN) has been proven to have better performance than the direct relationship (i.e. between query structure and database structures) that exists in similarity searching process. In this paper, we will focus on the implementation and improvement of the existing TSS. By adding in another layer of indirect relationship between the reference compound and the database compounds, along with an additional fusion layer, the performance of the new TSS strategy can be observed. The initial results indicated that there is an obvious increment in the recall value when applying the new strategy. The results are also evaluated with the significance test to show that the result produced by the new strategy is true and does not occurred by chance. Further work on different activity classes and different descriptors on the new strategy are expected to generate a better performance than the existing TSS.

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Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins.

Cited by 82 publications

References 1 publication

Large‐scale prediction of drug–target relationships

Large‐scale prediction of drug–target relationships

Capter 11 Filtering in Drug Discovery

New strategy for Turbo Similarity Searching: Implementation and testing

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