TargetNet Home Upload Result Tools Draw Heatmap Network Visualization Browse Help TargetNet Netting! Include models with: AUC >= Accuracy >= BEDROC >= MCC >= F-score >= Upload SMILES or SDF File Netting targets for one or more compounds stored in a SMILES or SDF file. Include models with: AUC >= Accuracy >= BEDROC >= MCC >= F-score >= Netting! Note 1: Each line of the .smi file should only contain the SMILES string and not contain the compound name. Example .smi file Example .sdf file Note 2: Currently, the upload module does not support Internet Explorer 8 and older versions. Please use at least IE9 or latest versions of Google Chrome, Mozilla Firefox and Apple Safari. Target Netting Save the result as an Excel (.xlsx) file or a tab-separated values (.tsv) file: Download Excel XLSX Download TSV Lipinski's Rule of Five Save the Rule of Five table as an Excel (.xlsx) file or a tab-separated values (.tsv) file: Download Excel XLSX Download TSV Copyright © 2013 - 2014 Computational Biology & Drug Design Group, Central South University. All rights reserved. Target Netting Heatmap For the target netting result, cluster the compounds and targets using hierchical clustering (Ward's method, Euclidean distance). Example TSV (1 compounds) Example TSV (5 compounds) Example TSV (50 compounds) Choose the .tsv file and click "Make Heatmap". It may take several seconds to render a large heatmap. Make Heatmap Drug-Target Interation Network Visualization The see the required input format of the network data, download the example below: Example Data (TSV format) Choose the .tsv file and click "Draw Network". Draw Network Network Introduction TargetNet: Target netting, rather than angling. TargetNet is a open web server that could be used for netting or predicting the binding of multiple targets for any given molecule. In drug discovery process, one of the challenges is to identify the potential targets for drug-like compounds. Once the target is successfully identified, several receptor-based drug design methods could be easily used to optimize the structure of compounds, aims at improving the biological activity of these compounds. However, this is usually a very difficult task for most of medicinal chemists. Herein, we attempted to address the difficulty by developing a open web server called TargetNet. TargetNet simultaneously constructs a large number of QSAR models based on current chemogenomics data to make future predictions. We used BindingDB database as our training datasets. BindingDB is a public, web-accessible database of measured binding affinities, focusing chiefly on the interactions of proteins considered to be candidate drug-targets with ligands that are small, drug-like molecules. Activity data were filtered to keep only activity end-point points that had half-maximum inhibitory concentration (IC50), half-maximum effective concentration (EC50) or Ki values. A compound was considered active when the mean activity value was below 10 uM. All compounds higher than 10 uM are considered inactive. Herein, to ensure that enough number of molecules could be used in model building, we previously selected those targets with larger than 200 biological activity data. Following this procedure, 109,061 compounds associated with 623 target proteins remained with 115,257 activity end-points, which were used for model building. A series of high confidence QSAR models were built using BindingDB. Random Forest models were built with FP2 fingerprints representations for 623 targets. For each model, we applied five-fold cross validation to evaluate the prediction performance of models. For 5-fold cross validation, the data set is split into 5 roughly equal-sized parts firstly, and then we fit the model to four parts of the data and calculate the error rate of the other part. The process is repeated 5 times so that every part can be predicted as a validation set. To observe the stability of models, we repeated the cross validation program 10 times to report standard deviations of each statistics. Finally, a model was built with all data and scored against itself – the training set and whole set should provide similar validation statistics. Statistics on the performance of the models include commonly used ones in classification schemes: accuracy, sensitivity, specificity, AUC, MCC and F-score values (see model performance overview section). The area under the receiver operating characteristic (ROC) curve provides an indication of the ability of the model to prioritize active compounds over inactive compounds. The ROC curve is the plot of the true positive versus the false positive rate. the Boltzmann-enhanced discrimination of ROC (BEDROC) solves the early enrichment issue by adding a weight to compounds recognized early. BEDROC was derived from the robust initial enhancement, and the sum of log of ranks test, which provided a statistical test to assess which method performs better than random. Furthermore, the cut-off providing the best MCC or F-score was reported, as they are shown to provide better performance. Model Performance Overview Tips: 1. Click on the legend labels in the upper right to hide or show the corresponding lines. 2. Move cursor in the chart to see the detailed performance values of each target. All Targets Copyright © 2013 - 2014 Computational Biology & Drug Design Group, Central South University. All rights reserved. Help TargetNet User Guide (HTML) Last revision: August 25, 2014. Copyright © 2013 - 2014 Computational Biology & Drug Design Group, Central South University. All rights reserved.