deepFPlearn - datasets, models, and configurations (Q6696650)

This repository contains all datasets used for training and testing the models all trained models, and the JSON configuration files used to train or test the models, as described in the original publication. Datasets dataset_train_ae.[pkl,tsv]: Data used for training the generic autoencoder dataset_train_fnn.[pkl,csv]: Data used for training the feed forward network (classification task) dataset_comptox.[pkl,tsv] : Unlabeled data used to provide first predictions dataset_predictions_comptox_all.csv : Results of predicting the unlabeled data with all models individually Models model_trained_ae*: trained Autoencoder (ae) model as published in https://doi.org/10.1093/bib/bbac257 model_trained_ae_weights.hdf5: the weights of the trained model, represents input for the prediction tasks model_trained_ae_saved_model.tar: the full model, can also be loaded and used for prediction model_trained_fnn_[AR, ER, GR, TR, Aromatase, PPARg, ED]*: Classification modelsas published in https://doi.org/10.1093/bib/bbac257 for the different targets androgen (AR), estrogen (ER), glucocorticoid (GR), and thyroid receptors (TR), Aromatase, PPARg, and more generally with endocrine disruption (ED). model_trained_fnn_[target]_model.weights.hdf5: the weights of the trained model, represents input for the prediction tasks model_trained_fnn_[target]_saved_model.tar: the full model, can also be loaded and used for prediction model_trained_fnn_[target]_history.[csv,svg]: the training history (all logged metrics) as table and x-y plot model_trained_fnn_[target]_predicted.testdata: the predictions on the test data after training Configuration files configFile_train_ae.json: Train the autoencoder configFile_train_fnn.json: Train the feed forward network without including the autoencoder (on uncompressed fingerprints) configFile_train_fnn_compressed.json: Train the feed forward network including the autoencoder (on compressed/encoded fingerprints) configFile_predict_comptox_[target].json: Use the trained autoencoder and each (w.r.t. target) of the trained feed forward models to predict the unlabeled comptox dataset