Conference proceeding
Computational prediction of ATC codes of drug-like compounds using tiered learning
2015 IEEE 5th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), pp.1-1
10/01/2015
DOI: 10.1109/ICCABS.2015.7344719
Abstract
The Anatomical Therapeutic Chemical (ATC) Code System is a World Health Organization (WHO) proposed classification that assigns codes to compounds based on their therapeutic, pharmacological and chemical characteristics as well as the in-vivo site of activity. The ability to predict the ATC code of an arbitrary compound with high accuracy can go a long way in selecting molecules for lead identification. We propose a computational approach to this problem that utilizes a natural pharmacological constraint, namely, that anatomical-therapeutic biological activity of certain types must preclude activities of many other types. The method proposed here utilizes machine learning in a tiered architecture; prediction of the ATC code at a certain level is constrained by the ATC code at the higher levels. Using this learning architecture, we have built classifiers that incorporate information from a compound's structure, as well as its chemical and protein interactions. The proposed approach has been validated using 2335 drugs from the ChEMBL database in both cross-validation and test setting. The prediction accuracy obtained with this approach is 78.72% and is comparable or better than the prediction accuracy of other methods at the state of the art.
Details
- Title: Subtitle
- Computational prediction of ATC codes of drug-like compounds using tiered learning
- Creators
- Thomas Olson - San Francisco State UniversityRahul Singh - San Francisco State University
- Resource Type
- Conference proceeding
- Publication Details
- 2015 IEEE 5th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), pp.1-1
- Publisher
- IEEE
- DOI
- 10.1109/ICCABS.2015.7344719
- Language
- English
- Date published
- 10/01/2015
- Academic Unit
- Computer Science
- Record Identifier
- 9984446515902771
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