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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.


Comparsion of WES with 1NN.The ture positive rate of WES (red) and 1NN (blue) are shown as bars along with the similarity bins (x-axis).
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f2: Comparsion of WES with 1NN.The ture positive rate of WES (red) and 1NN (blue) are shown as bars along with the similarity bins (x-axis).

Mentions: In multi-objective pattern recognition, the k-Nearest Neighbors algorithm (k-NN) is a non-parametric and widely used method. The output depends on whether k-NN is used for classification by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). WES has been compared to a one nearest neighbor (1NN) model (Fig. 2), which judges the probability of a drug targeting to a protein based only on the maximum similarity to the reference ligands of the target. For close analogs, Tanimoto coefficients (Tc) > 0.65, the fraction of true positives was comparable between 1NN and WES (Fig. 2). Surprisingly, by across most similarity thresholds, WES substantially outperforms 1NN. Notably, among the correct drug-target predictions by WES, 4,319 of them show low similarity (Tc < 0.4) with the ligand sets of their respective targets. However, the proportion held by 1NN is zero. These results prove that WES is more capable of predicting drug targets for various structurally diverse chemicals.


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)

Comparsion of WES with 1NN.The ture positive rate of WES (red) and 1NN (blue) are shown as bars along with the similarity bins (x-axis).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Comparsion of WES with 1NN.The ture positive rate of WES (red) and 1NN (blue) are shown as bars along with the similarity bins (x-axis).
Mentions: In multi-objective pattern recognition, the k-Nearest Neighbors algorithm (k-NN) is a non-parametric and widely used method. The output depends on whether k-NN is used for classification by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). WES has been compared to a one nearest neighbor (1NN) model (Fig. 2), which judges the probability of a drug targeting to a protein based only on the maximum similarity to the reference ligands of the target. For close analogs, Tanimoto coefficients (Tc) > 0.65, the fraction of true positives was comparable between 1NN and WES (Fig. 2). Surprisingly, by across most similarity thresholds, WES substantially outperforms 1NN. Notably, among the correct drug-target predictions by WES, 4,319 of them show low similarity (Tc < 0.4) with the ligand sets of their respective targets. However, the proportion held by 1NN is zero. These results prove that WES is more capable of predicting drug targets for various structurally diverse chemicals.

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.