Here, we present SuperPred, which is a prediction webserver for ATC code and target predicition of compounds.
Predicting ATC codes or targets of small molecules and thus gaining information about the compounds offers assistance in the drug development process.
The webserver's ATC predicition as well as target prediction is based on a machine learning model, using logistic regression and ECFP4 fingerprints.
The drug classification for a compound can be performed at the Drug Classification site. Target prediction for an input compound can be executed at the Target-Prediction site.
Statistics of the testset and the cross-validation results can be found at the statistics page.
If you have any questions please see the FAQs or feel free to contact us!
The ATC code prediction is based on machine learning, using a linear logistic regression model. It is trained on ECFP4 fingerprints from 1552 different drugs in 233 different level 4 ATC classes. Query compounds are evaluated and scored by the machine learning model, ranking each ATC class and returning the highest scoring classes.
The Anatomical Therapeutic Chemical (ATC) classification system is used for the classification of drugs. It is published by the World Health Organization (WHO). The classification is based on therapeutic and chemical characteristics of the drugs. Each ATC code is divided into 5 levels:
1. level: Anatomical main group
2. level: Therapeutic main group
3. level: Therapeutic/pharmacological subgroup
4. level: Chemical/therapeutic/pharmacological subgroup
5. level: Chemical substance
Substances or combination of substances in the 5th level refer to a single indication. Drugs having more than one indication belong to more than one ATC code. Aspirine for example has 3 ATC codes assigned.