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

<p>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.</p> <p><strong>The datasets used for model construction, prediction and screening of chemical library are provided in this uploaded data in <a href="../api/records/10947610/draft/files/Molecular_image_Custom_DCNN_datasets.zip/content" target="_blank" rel="noopener noreferrer">Molecular_image_Custom_DCNN_datasets.zip</a> file for Custom-DCNN models and <a href="../api/records/10947610/draft/files/Molecular_image_pre_trained_datasets.zip/content" target="_blank" rel="noopener noreferrer">Molecular_image_pre_trained_datasets.zip</a> file for Pre-trained fine-tuned models. </strong><strong>The final run of models results given in file <a href="../api/records/10947610/draft/files/Custom_DCNN_Pre_trained_models.zip/content" target="_blank" rel="noopener noreferrer">Custom_DCNN_Pre_trained_models.zip</a></strong></p>