Predicting Drug-Drug Interaction
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PTB-DDI is an promising framework for your medical research, predicting drug-drug interaction probability based on the drug SMILES sequence.
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Introduction
The simultaneous use of two or more drugs in clinical treatment may lead to severe adverse reactions. Predicting drug-drug interactions (DDIs) is crucial to avoid adverse events in combination therapy. Traditional pharmaceutical methods are costly and time-consuming, leading to the popularity of deep-learning methods in recent years. We propose an accurate and simple framework called PTB-DDI for drug-drug interaction prediction. The purpose of establishing this website is that because pharmaceutical companies and researchers have some drugs, but there are no reported literature or other websites for reference, we provide them with reference to the possibility of predicting interactions between drugs. They can use the PTB-DDI framework on our website to predict whether there are any interactions.
The website has following functions:
- Input the SMILES to predict the drug-drug interaction probability.
- Draw molecules to predict the drug-drug interaction probability.
- Process batch data to quickly predict large amounts of chemical structure data through upload CSV file.
- Choose the parameter-sharing and parameter-independent two type models to predict the drug-drug interaction probablity.
The results are only for reference.
Citation
If you use the service of this website, please cite the following paper:
Qiu, Jiayue and Yan, Xiao and Tian, Yanan and Li, Qin and Liu, Xiaomeng and Yang, Yuwei and Tong, Henry H.Y. and Liu, Huanxiang.
This work was supported by Macao Science and Technology Development Fund (0043/2023/AFJ), Macao Polytechnic University (No. RP/ FCA-02/2023).