Dataset Information

Drug-drug interaction through molecular structure similarity analysis.

ABSTRACT:

Background

Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.

Methods

We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates.

Results

The methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database.

Conclusion

The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58?403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.

SUBMITTER: Vilar S

PROVIDER: S-EPMC3534468 | biostudies-literature | 2012 Nov-Dec

REPOSITORIES: biostudies-literature

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Publications

Drug-drug interaction through molecular structure similarity analysis.

Vilar Santiago S Harpaz Rave R Uriarte Eugenio E Santana Lourdes L Rabadan Raul R Friedman Carol C

Journal of the American Medical Informatics Association : JAMIA 20120530 6

<h4>Background</h4>Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs.<h4>Methods</h4>We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in establis ...[more]

PMID: 22647690

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Project description:BACKGROUND:Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples. RESULTS:We propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate the accumulative similarity with all known drug-target interactions for each unobserved drug-target interaction. On the other hand, we obtain the signed distance from OCSVM learned from the known interactions with high recall (?0.95) for each unobserved drug-target interaction. After normalizing all accumulative similarities and signed distances to the range [0,1], we compute the score for each unobserved drug-target interaction via averaging its accumulative similarity and signed distance. Unobserved interactions with lower scores are preferentially served as reliable negative samples for the classification algorithms. The performance of the proposed method is evaluated on the interaction data between 1094 drugs and 1556 target proteins. Extensive comparison experiments using four classical classifiers and one domain predictive method demonstrate the superior performance of the proposed method. A better decision boundary has been learned from the constructed reliable negative samples. CONCLUSIONS:Proper construction of highly-reliable negative samples can help the classification models learn a clear decision boundary which contributes to the performance improvement.

Dataset Information

Drug-drug interaction through molecular structure similarity analysis.

Background

Methods

Results

Conclusion

Publications

Drug-drug interaction through molecular structure similarity analysis.

Similar Datasets

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