"iDCNNPred: An interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer"

In this study, we proposed a novel interpretable deep convolutional neural network prediction (iDCNNPred) system for classifying molecular bioactivity and identifying predictive potential inhibitors for the PI3Ka isoform protein. This system utilizes 2D molecular image representation as input features, instead of traditional molecular fingerprints or descriptors.

The datasets used for model construction, prediction and screening of chemical library are provided in this uploaded data in Molecular_image_Custom_DCNN_datasets.zip file for Custom-DCNN models and Molecular_image_pre_trained_datasets.zip file for Pre-trained fine-tuned models. The final run of models results given in file Custom_DCNN_Pre_trained_models.zip