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Large-scale Direct Targeting for Drug Repositioning and Discovery.

Zheng C, Guo Z, Huang C, Wu Z, Li Y, Chen X, Fu Y, Ru J, Ali Shar P, Wang Y, Wang Y - Sci Rep (2015)

Bottom Line: However, the biological means on a large scale remains challenging and expensive even nowadays.The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale.This shows that the WES method provides a potential in silico model for drug repositioning and discovery.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Center, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, 712100, China.

ABSTRACT
A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale. To address these problems, in this work, a novel algorithm termed weighted ensemble similarity (WES) has been developed to identify drug direct targets based on a large-scale of 98,327 drug-target relationships. WES includes: (1) identifying the key ligand structural features that are highly-related to the pharmacological properties in a framework of ensemble; (2) determining a drug's affiliation of a target by evaluation of the overall similarity (ensemble) rather than a single ligand judgment; and (3) integrating the standardized ensemble similarities (Z score) by Bayesian network and multi-variate kernel approach to make predictions. All these lead WES to predict drug direct targets with external and experimental test accuracies of 70% and 71%, respectively. This shows that the WES method provides a potential in silico model for drug repositioning and discovery.

No MeSH data available.


Non-intuitive (Hydrocortamate) and straightforward (Saquinavir) WES prediction, with Tc values to closest references.
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f3: Non-intuitive (Hydrocortamate) and straightforward (Saquinavir) WES prediction, with Tc values to closest references.

Mentions: In order to further assess the ligand scaffold hopping (LSH) ability for WES model, we have compared the predicted ligands with those known ligands for the same targets. The results show a diversified structural scaffolds as shown in Table S2-3. This indicates that WES catches the relatively complete drug-binding features for a protein from the ensemble level not from its single ligand like 1NN method. For example, drug Hydrocortamate, which is predicted to modulate Enpp2 (Fig. 3), is only marginally similar to the known ligand sets (Tc value 0.47; Fig. 3). Clearly, those similar compounds are more easily identified by WES. For example, Saquinavir, closely resemble (Tc value 0.91; Fig. 3) to the ligand set of REN, is predicted to regulate REN (Fig. 3). The LSH analysis confirms the specificity of prediction for WES, which is important for drug repositioning for those known drugs in pharmaceutical researches.


Large-scale Direct Targeting for Drug Repositioning and Discovery.

Zheng C, Guo Z, Huang C, Wu Z, Li Y, Chen X, Fu Y, Ru J, Ali Shar P, Wang Y, Wang Y - Sci Rep (2015)

Non-intuitive (Hydrocortamate) and straightforward (Saquinavir) WES prediction, with Tc values to closest references.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496667&req=5

f3: Non-intuitive (Hydrocortamate) and straightforward (Saquinavir) WES prediction, with Tc values to closest references.
Mentions: In order to further assess the ligand scaffold hopping (LSH) ability for WES model, we have compared the predicted ligands with those known ligands for the same targets. The results show a diversified structural scaffolds as shown in Table S2-3. This indicates that WES catches the relatively complete drug-binding features for a protein from the ensemble level not from its single ligand like 1NN method. For example, drug Hydrocortamate, which is predicted to modulate Enpp2 (Fig. 3), is only marginally similar to the known ligand sets (Tc value 0.47; Fig. 3). Clearly, those similar compounds are more easily identified by WES. For example, Saquinavir, closely resemble (Tc value 0.91; Fig. 3) to the ligand set of REN, is predicted to regulate REN (Fig. 3). The LSH analysis confirms the specificity of prediction for WES, which is important for drug repositioning for those known drugs in pharmaceutical researches.

Bottom Line: However, the biological means on a large scale remains challenging and expensive even nowadays.The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale.This shows that the WES method provides a potential in silico model for drug repositioning and discovery.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Center, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, 712100, China.

ABSTRACT
A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale. To address these problems, in this work, a novel algorithm termed weighted ensemble similarity (WES) has been developed to identify drug direct targets based on a large-scale of 98,327 drug-target relationships. WES includes: (1) identifying the key ligand structural features that are highly-related to the pharmacological properties in a framework of ensemble; (2) determining a drug's affiliation of a target by evaluation of the overall similarity (ensemble) rather than a single ligand judgment; and (3) integrating the standardized ensemble similarities (Z score) by Bayesian network and multi-variate kernel approach to make predictions. All these lead WES to predict drug direct targets with external and experimental test accuracies of 70% and 71%, respectively. This shows that the WES method provides a potential in silico model for drug repositioning and discovery.

No MeSH data available.