Paccmann_MCA

This repository demonstrates how to use the IMPROVE library v0.0.3-beta for building a drug response prediction (DRP) model using Paccmann_MCA, and provides examples with the benchmark cross-study analysis (CSA) dataset.

This version, tagged as v0.0.3-beta, is the final release before transitioning to v0.1.0-alpha, which introduces a new API. Version v0.0.3-beta and all previous releases have served as the foundation for developing essential components of the IMPROVE software stack. Subsequent releases build on this legacy with an updated API, designed to encourage broader adoption of IMPROVE and its curated models by the research community.

A more detailed tutorial can be found here.

Drug interaction prediction with PaccMann

paccmann_predictor is a package for drug interaction prediction, with examples of anticancer drug sensitivity prediction and drug target affinity prediction. Please see the paper:

• Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders (Molecular Pharmaceutics, 2019). This is the original paper on IC50 prediction using drug properties and tissue-specific cell line information (gene expression profiles). While the original code was written in tensorflow and is available here, this is the pytorch implementation of the best PaccMann architecture (multiscale convolutional encoder).

NOTE: PaccMann acronyms "Prediction of AntiCancer Compound sensitivity with Multi-modal Attention-based Neural Networks". This model is curated as a part of the IMPROVE Project The original model code is here

Dependencies

Requirements

• conda>=3.7

Create a conda environment:

conda env create -f examples/IC50/conda.yml

Activate the environment:

conda activate paccmann_predictor
pip install -e .

Install CANDLE library

pip install git+https://github.com/ECP-CANDLE/candle_lib@develop

ML framework:

• Torch -- deep learning framework for building the prediction model

IMPROVE dependencies:

• IMPROVE v0.0.3-beta
• candle_lib - IMPROVE dependency (enables various hyperparameter optimization on HPC machines)

Dataset

Benchmark data for cross-study analysis (CSA) can be downloaded from this site.

The data tree is shown below:

csa_data/raw_data/
├── splits
│   ├── CCLE_all.txt
│   ├── CCLE_split_0_test.txt
│   ├── CCLE_split_0_train.txt
│   ├── CCLE_split_0_val.txt
│   ├── CCLE_split_1_test.txt
│   ├── CCLE_split_1_train.txt
│   ├── CCLE_split_1_val.txt
│   ├── ...
│   ├── GDSCv2_split_9_test.txt
│   ├── GDSCv2_split_9_train.txt
│   └── GDSCv2_split_9_val.txt
├── x_data
│   ├── cancer_copy_number.tsv
│   ├── cancer_discretized_copy_number.tsv
│   ├── cancer_DNA_methylation.tsv
│   ├── cancer_gene_expression.tsv
│   ├── cancer_miRNA_expression.tsv
│   ├── cancer_mutation_count.tsv
│   ├── cancer_mutation_long_format.tsv
│   ├── cancer_mutation.parquet
│   ├── cancer_RPPA.tsv
│   ├── drug_ecfp4_nbits512.tsv
│   ├── drug_info.tsv
│   ├── drug_mordred_descriptor.tsv
│   └── drug_SMILES.tsv
└── y_data
    └── response.tsv

Model scripts and parameter file

• Paccmann_MCA_preprocess_improve.py - takes benchmark data files and transforms into files for training and inference
• Paccmann_MCA_train_improve.py - trains the Paccmann_MCA model
• Paccmann_MCA_infer_improve.py - runs inference with the trained model
• Paccmann_MCA_default_model_csa.txt - default parameter file

Step-by-step instructions

1. Clone the model repository

git clone https://github.com/JDACS4C-IMPROVE/Paccmann_MCA.git
cd Paccmann_MCA
git checkout v0.0.3-beta

2. Activate environment

Activate conda env

conda activate paccmann_predictor

3. Run setup_improve.sh.

source setup_improve.sh

This will:

1. Download cross-study analysis (CSA) benchmark data into ./csa_data/.
2. Clone IMPROVE repo (checkout tag v0.0.3-beta) outside the Paccmann_MCA model repo
3. Set up env variables: IMPROVE_DATA_DIR (to ./csa_data/) and PYTHONPATH (adds IMPROVE repo).

4. Preprocess CSA benchmark data (raw data) to construct model input data (ML data)

python Paccmann_MCA_preprocess_improve.py

Preprocesses the CSA data and creates train, validation (val), and test datasets.

Generates:

• three model input data files, each containing the drug response values (i.e. AUC) and corresponding metadata: train_y_data.csv, val_y_data.csv, test_y_data.csv
• gene_expression and drug SMILES data
• downloads and preprocesses model specifile files required for Paccmann_MCA.

ml_data
└── GDSCv1-CCLE
    └── split_0
        ├── test_y_data.csv
        ├── train_y_data.csv
        ├── val_y_data.csv
        ├── gene_expression.csv
        ├── smiles.smi
        ├── 2128_genes.pkl
        └── smiles_language_chembl_gdsc_ccle.pkl

5. Train model

python Paccmann_MCA_train_improve.py

Trains model using the preprocessed model input data.

Generates:

• trained model: model.pt
• predictions on val data (tabular data): val_y_data_predicted.csv
• prediction performance scores on val data: val_scores.json

out_models
└── GDSCv1
    └── split_0
        ├── model.pt
        ├── final_params.pickle
        ├── val_scores.json
        └── val_y_data_predicted.csv

6. Run inference on test data with the trained model

python Paccmann_MCA_infer_improve.py

Evaluates the performance on a test dataset with the trained model.

Generates:

• predictions on test data (tabular data): test_y_data_predicted.csv
• prediction performance scores on test data: test_scores.json

out_infer
└── GDSCv1-CCLE
    └── split_0
        ├── test_scores.json
        └── test_y_data_predicted.csv

References

@article{manica2019paccmann,
  title={Toward explainable anticancer compound sensitivity prediction via multimodal attention-based convolutional encoders},
  author={Manica, Matteo and Oskooei, Ali and Born, Jannis and Subramanian, Vigneshwari and S{\'a}ez-Rodr{\'\i}guez, Julio and Mart{\'\i}nez, Mar{\'\i}a Rodr{\'\i}guez},
  journal={Molecular pharmaceutics},
  volume={16},
  number={12},
  pages={4797--4806},
  year={2019},
  publisher={ACS Publications},
  doi = {10.1021/acs.molpharmaceut.9b00520},
  note = {PMID: 31618586}
}

@article{born2021datadriven,
  author = {Born, Jannis and Manica, Matteo and Cadow, Joris and Markert, Greta and Mill, Nil Adell and Filipavicius, Modestas and Janakarajan, Nikita and Cardinale, Antonio and Laino, Teodoro and {Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a},
  doi = {10.1088/2632-2153/abe808},
  issn = {2632-2153},
  journal = {Machine Learning: Science and Technology},
  number = {2},
  pages = {025024},
  title = {{Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2}},
  url = {https://iopscience.iop.org/article/10.1088/2632-2153/abe808},
  volume = {2},
  year = {2021}
}