DL-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 best model trained on 475 drugs predicted an external validation set of 198 drugs with an accuracy of 86.9%, sensitivity of 82.5%, specificity of 92.9%, and area under the curve of 0.955, which is better than the performance of previously described DILI prediction models.

Input molecule data
Input File
Input Format

1.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).
2. It is recommended to use the obabel program (http://openbabel.org/wiki/Main_Page) to format the SMILES files for better and faster prediction.
3. 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).
4. DL-Combined model: Predicting DILI of pharmaceutical compounds using Chen et al.'s DILI annotation (Chen et al., Toxicological Sciences 2013, 136, 242-249).
5. For more information of DL-DILI server, please refer to "Youjun Xu, Ziwei Dai, Fangjin Chen, Shuaishi Gao, Jianfeng Pei, and Luhua Lai, Deep Learning for Drug-Induced Liver Injury, J. Chem. Inf. Model., 2015, 55 (10), pp 2085-2093".
6. This webserver is freely acceessible to academics. For industry, please send inquires to jfpei@pku.edu.cn.