================= PaccMann-MCA ================= Prediction of AntiCancer Compound sensitivity with Multi-modal Attention-based Neural Networks Model Architecture -------------------- The PaccMann-MCA model is composed of a network propagation layer, an attention-based gene expression encoder, a SMILES embedding layer and a SMILES encoder. The gene expression encoder generates attention weights to encode the cell-line gene expression. The model uses multiscale convolutional attention (MCA) mechanism for the SMILES encoders. The MCA mechanism enables to incorporate both positional information and long-range dependencies using a combination of convolutional layers and contextual attention mechanisms. Finally the encoded drug and cell-line features are concatenated and inputted to fully connected dense layers to predict the drug response. Feature Representation -------------------- * Cancer features: * Gene Expression: RMA-normalized * Drug features: * SMILES * Morgan fingerprints URLs -------------------- - `Original GitHub `__ - `IMPROVE GitHub `__ References -------------------- `1. `_ M. Manica, et. al. "Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders", Mol. Pharmaceutics, 2019