DILI Prediction Server

Introduction of DL-DILI

Drug-induced Liver Injury (DILI) is the single most frequent cause of safety-related drug marketing withdrawals. DL-DILI webserver uses deep learning (DL) methods to predict DILI. Two DL-DILI models (DL-Combined and DL-Liew), which were developed based on different datasets and DILI annotations are available in this server. The DL-combined model trained on 451 (original: 475) drugs and predicted an external validation set of 198 drugs with an accuracy of 86.4% (original: 86.9%), sensitivity of 83.3% (original: 82.5%), specificity of 90.5% (original: 92.9%), and area under the curve of 0.935 (original: 0.955), which is better than the performance of previously described DILI prediction models. The DL-Liew model trained on 1065 compounds with higher diversity than DL-combined and tested 119 compounds with an accuracy of 77.3%, sensitivity of 81.4%, specificity of 71.4% and area under the curve of 0.855. The revised dataset can be downloaded here.

*Note.
1. The format of an input file is allowed, such as "*.smi", "*.mol", "*.mol2", "*.sdf"; The size of an input file is less than 200KB;
2. The"input File" option allows you to upload a file including one or more molecules (the number of heavy atoms must be less than 100).
3. It is recommended to use OpenBabel toolkit (http://openbabel.org/wiki/Main_Page) to format the SMILES files for better and faster prediction.
4. DL-Liew model: Predicting DILI of compounds using Liew et al.'s DILI annotation (Liew et al., Journal of Computer-Aided Molecular Design 2011, 25, 855-871).
5. DL-Combined model: Predicting DILI of pharmaceutical compounds using Chen et al.'s DILI annotation (Chen et al., Toxicological Sciences 2013, 136, 242-249).
6. This web server is freely accessible to academics. For industry, please send inquires to jfpei@pku.edu.cn.

DL-DILI Prediction Server

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Input molecule data
Input File
Model Type