MOFGCN

RECOVER

Exploring the space of drug combinations to discover synergistic drugs using Active Learning

TGSA

PyTorch implementation of "TGSA: Protein-Protein Association-Based Twin Graph Neural Networks for Drug Response Prediction with Similarity Augmentation"

DeepAEG

DeepAEG: A model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies

drGAT

drGT is a deep learning model that predicts drug response (sensitivity) in cancer cell lines while providing gene-level explanations through attention mechanisms. It constructs a heterogeneous graph connecting drugs, cells, and genes, enabling both accurate predictions and interpretable insights into which genes influence drug efficacy. ## Key Features - **Attention-based GNNs** — Utilizes GAT, GATv2, or Transformer architectures to model complex relationships in the drug-cell-gene network. - **Drug Response Prediction** — Predicts drug sensitivity (binary or continuous) across cell lines and drugs. - **Gene Explainability** — Uncovers gene-level contributions to predictions via attention scores, aiding biological interpretation. - **Pretrained Models** — Includes pretrained models for inference without retraining on supported datasets. - **Flexible Training** — Supports custom datasets, various data-splitting strategies (random, leave-drug/cell-out), and architecture modifications. - **GPU Acceleration** — Optimized for GPU execution via PyTorch and PyTorch Geometric for faster training and inference. ## Philosophy drGT emphasizes interpretability alongside predictive accuracy, using attention mechanisms to bridge deep learning with actionable biological insights in drug discovery.