# Research Object Crate for SynProtX

Original URL: https://workflowhub.eu/workflows/1726/ro_crate?version=3

# SynProtX [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13285494.svg)](https://doi.org/10.5281/zenodo.13285494) An official implementation of our research paper **"SynProtX: A Large-Scale Proteomics-Based Deep Learning Model for Predicting Synergistic Anticancer Drug Combinations"**. SynProtX is a deep learning model that integrates large-scale proteomics data, molecular graphs, and chemical fingerprints to predict synergistic effects of anticancer drug combinations. It provides robust performance across tissue-specific and study-specific datasets, enhancing reproducibility and biological relevance in drug synergy prediction. ## Setting up environment We use [Miniconda](https://docs.anaconda.com/miniconda/) to manage Python dependencies in this project. To reproduce our environment, please run the following script in the terminal: ```sh conda env create -f env.yml conda activate SynProtX ``` ## Downloading raw data Datasets, hyperparameters, and model checkpoints can be downloaded through [Zenodo](https://doi.org/10.5281/zenodo.13285494). ## Generating dataset A tarball will be obtained after download. After file extraction, move all nested folders to the root of this project directory. You might need to move all files in `data/export` up to `data` folder. Otherwise, you will run the Jupyter Notebook files to generate mandatory data. Let’s take a look at `ipynb` folder. Run the following files in order if you want to replicate our exported data. - `01_drugcomb_clean.ipynb` → `cleandata_cancer.csv` - `02_CCLE_gene_expression` → `CCLE_expression_cleaned.csv` - `03_omics_preprocess` → `protein_omics_data_cleaned.csv` - `04_drugcomb_gene_prot_clean` → `data_preprocessing_gene.pkl`, `data_drugcomb.pkl`, `data_preprocessing_protein.pkl` - `05_graph_generate.ipynb` → `nps_intersected` folder - `06_smiles_feat_generate.ipynb` → `smiles_graph_data.pkl` - `07_to_ecfp6_deepsyn.ipynb` → `deepsyn_drug_row.npy`, `deepsyn_drug_col.npy` > If the console shows an error indicating that SMILES not found, you MUST run the file `06_smiles_feat_generate.ipynb` again to regenerate data. ## Training and testing To execute a training and testing task for our model, run the following script ```sh python synprotx/.py -d -m ``` Possible options are listed below. - `model` represents the name of the model to run. Must be one of `gat`, `gcn`, `attentivefp` and `gatfp`. - `--database`/`-d` specifies data source to train the model on. Must be one of `almanac-breast`, `almanac-lung`, `almanac-ovary`, `almanac-skin`, `friedman`, `oneil`. - `--mode`/`-m` input must be either `clas`, for classification task, or `regr`, for regression task. Default to `clas` - Flags `--no-feamol`, `--no-feagene`, `--no-feaprot` disable the molecule branch, gene expression branch, and protein expression branch, respectively, when propagate through the model. **Note:** There are more options to configure. Execute `python synprotx/.py -h` for a more detailed description.

Author
License
  MIT

## Contents

Main Workflow: SynProtX
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Main Workflow Diagram: synprotx_architect.png
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