Drug Response Prediction Models ================================= The IMPROVE project has curated a number of Drug Response Prediction (DRP) models. Model curation involves selecting a subset of community models and then modifying their software scripts to conform to a unified code structure. .. The primary focus is on pan-cancer pan-drug models. Descriptions of the curated models and links to the code can be found below. Model Selection ----------------- We previously compiled a compendium of papers that utilized DL methods for DRP [`1 `_]. As of December 2023, this collection comprised more than 90 models. Considering the size of this collection, we selected a subset of models to work on based on qualitative and hands-on selection criteria. Qualitative selection criteria: - Models providing open-source code with: 1) comprehensive installation instructions of the computational environment, 2) data preprocessing scripts that take feature and response data and transform them into model input data, 3) scripts which execute model training. - Deep learning models implemented in TensorFlow/Keras or PyTorch. - Focus on pan-cancer multi-drug models utilizing cancer and drug representations as input features. - Preference for models utilizing conventional features and predicting continuous treatment response in cell lines (e.g., AUC, IC50). - End-to-end learning models, excluding those requiring feature pre-training or utilizing transfer learning. - Preference for recent peer-reviewed publications. Hands-on selection criteria: - Successful installation of the computational environment - Execution of preprocessing scripts to generating model-input data - Reproducibility of key results as reported in the respective papers Model Standardization ----------------------- Once the models met the selection criteria and were assessed for reproducibility, we proceeded with standardizing the model code structure. The standardization process is executed as follows: - Establishing reference prediction performance using the original model (e.g., r-square) - Restructuring the code into distinct preprocessing, training, and inference scripts following the IMPROVE guidelines - Verifying that the restructured code reproduces the reference performance - Run small-scale cross-study analysis (a simple python script) - Conducting a small-scale cross-study analysis (using a simple Python script) - Running IMPROVE library test scripts Further guidelines and details regarding code restructuring while leveraging the IMPROVE library are discussed in the :doc:`Tutorial `. Curated Models ----------------------- Currently, we utilized 9 models for the cross-study analysis. We forked the original repositories and conducted the model selection and standardization procedures as discussed above. .. toctree:: :titlesonly: Models-GraphDRP Models-tCNNS Models-DeepTTC Models-IGTD Models-HiDRA Models-Paccman_MCA Models-DualGCN Models-DeepCDR Models-PathDSP Models-LGBM Models-UNO References --------------------------------- `1. `_ A. Partin et al. "Deep learning methods for drug response prediction in cancer: Predominant and emerging trends", Frontiers in Medicine, Section Prediction Oncology, 2023