Polypharmacology Browser 3 (PPB3) uses deep learning techniques, specifically deep neural network (DNN) models and it takes the SMILES representation of the compounds as an input and predicts top 20 targets that are ranked based on the prediction confidence score.
In PPB3, DNN models are trained using reference data sourced from the ChEMBL database. PPB3 uses 7 DNN models to predict potential targets for any given query molecule. Each DNN model is structured with an input layer (molecular fingerprints), two hidden layers and an output layer (targets). In total, we used 7 different fingerprints to train our DNN models: ECFP4, Atom Pair, Layered, RDKit, MHFP6, ECFP6 and the combination of ECFP4 and MHFP6 fingerprints, known as fused fingerprint. When a user inputs a query compound, its molecular fingerprint is fed into the input layer of each DNN model. The data then passes through the hidden layers, where the model analyzes the features and identifies potential targets. Finally, In the output layer, the model generates predictions along with confidence scores for each predicted target. PPB3 is built using the latest data extracted from ChEMBL version 34 using 7,546 targets labeled with 15 unique target types and all the source organisms, 1,187,089 compounds and 2,496,555 target-compound interactions.
The prediction results page includes a table displaying the top 20 predicted targets ranked by confidence score. The table provides the target's ChEMBL ID (linked directly to the target's ChEMBL report card), full name, protein class, organism, type, and the nearest neighbors of the query compound ranked by Tanimoto similarity alongside with the compounds’ ChEMBL report card. At the top of the results page, pie charts provide an overview of the predicted targets' protein classes, organisms, and types. Users can save the predictions as an Excel file by clicking the "Save the Results" button.
PPB3 is accessible via: https://ppb3.gdb.tools