Drug metabolism via the cytochrome P450 system has emerged as an important determinant in the occurrence of several drug interactions that can result in drug toxicities, reduced pharmacological effect, and adverse drug reactions. Recognizing whether the drugs involved act as enzyme substrates, inducers, or inhibitors can prevent clinically significant interactions from occurring. The SuperCYPsPred web server is focused on five major CYPs isoforms, including 1A2, 2C9, 2C19, 2D6 and 3A4, that are responsible for more than 90% of the metabolism of clinical drugs. Besides the predictions, the web server provides literature curated details on known cytochrome interaction network of approved drugs.
We have developed prediction models for classification of the CYPs (1A2, 2C9, 2C19, 2D6 and 3A4) inhibitors and non-inhibitors using machine-learning method. To predict a compound, please click the prediction button
We have analysed and validated our models using eight different sampling methods and four different machine learning algorithms.Two different feature based fingerprints are used to build respective models. The user can select the fingerprints of their choice and can perform the prediction on this platform. To know more about our models, method and data set used please go to Model info
Many drugs also inhibit or induce the activity of CYPs, which is important to health professionals trying to dose medicines. If a drug induces a CYP that is also active in another drug's metabolism, the dosage of the first drug must be enhanced to achieve a therapeutic effect. In case of inhibition of a CYPs, the dosage of the drugs can be reduced, which also lowers side effects. To check whether the metabolisms of the different combinations of drugs interact with each other, please use our DDI search platform. The DDI is implemented using a manually curated data set published as SuperCYP Database