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Multi-aspect candidates for repositioning: data fusion methods using heterogeneous information sources.

Á Arany¹, B Bolgár, B Balogh

¹Department of Organic Chemistry, University Semmelweis H-1092 Budapest, Hogyes Endre u. 7, Hungary.

Current Medicinal Chemistry

|December 6, 2012

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This study introduces a novel kernel-based data fusion method to enhance drug repositioning strategies. This approach efficiently integrates diverse information sources, improving the identification of new therapeutic applications for existing drugs.

Area of Science:

Pharmacology
Computational Chemistry
Drug Discovery

Background:

Drug repositioning is a cost-effective strategy in pharmaceutical research and development.
Effectively utilizing diverse data sources like chemical, target, and bibliometric information can promote drug repositioning.
Existing methods for integrating these diverse sources have limitations.

Purpose of the Study:

To develop and evaluate a novel, efficient approach for identifying new therapeutic applications of approved drugs and drug candidates.
To adapt a kernel-based data fusion method for drug repositioning.
To demonstrate the advantages of this method over standard approaches.

Main Methods:

A kernel-based data fusion strategy was employed, featuring multiple representations of information sources.

Related Experiment Videos

Automated weighting and statistically optimal combination of various information sources were implemented.

Automated weighting of query compound components was also incorporated.

Performance was evaluated using the Anatomical Therapeutic Chemical Classification System in a cross-validation framework.

Main Results:

Kernel-based data fusion significantly outperforms standard rank-based fusion in integrating heterogeneous information sources.
The method demonstrates superior efficiency in identifying potential new therapeutic uses for drugs.
The approach is effective for both drug repositioning and de novo drug discovery.

Conclusions:

Kernel-based data fusion offers a unique and powerful solution for drug repositioning.
This method provides a robust framework for integrating diverse data in drug discovery.
The approach has significant potential for pharmaceutical applications and further research.