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AlphaTherapy

Introduction

AlphaTherapy is a theoretical proof-of-concept AI framework for the rational design of sequential drug treatments for tumors across diverse drugs and tumor types.

Users can create a scenario by customizing input parameters tailored to their specific problem related to sequential therapy. Specifically, AlphaTherapy is designed to investigate sequential drug treatments applicable for different scenarios, including for a specified cell line (parameter: cell line), for a specified treatment duration (parameter: terminal step) and for a constrained sequential drug combination space (parameter: drug pool).

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Table of contents

Requirements

  • Python == 3.6.13
  • torch == 1.10.2
  • gym == 0.15.4
  • tensorboard == 2.2.2
  • rdkit == 2021.03.5
  • openpyxl, numpy, pandas, scipy, matplotlib, scikit-learn Python modules are also needed
  • R == 4.3.1
  • clusterProfiler, org.Hs.eg.db, usefun R packages are also needed

Installation from Docker image

docker pull xiaohanchen/alphatherapy:v1

Installation from github

1. Downloading AlphaTherapy

git clone https://github.com/bm2-lab/AlphaTherapy.git

2. Installing some packages

cd ./scripts/gym_cell_model
pip install -e .
cd ../tianshou-master
pip install -e .

Usage

1. Rational design of sequential drug treatments for tumors by AlphaTherapy

This section will generate a list of effective sequential drug combinations for user-specified scenarios, sorted by episode reward. For illustration, we use a specific scenario with the following three key parameters:

  1. Drug Pool: A drug set containing 395 drugs (387 anticancer drugs approved by FDA and 8 other drugs).
  2. Cell Line: MCF7, a commonly used breast cancer cell line.
  3. Termination Step: Step 2, indicating the drug duration time. One step indicates 24 hour drug administration.

1.1 Drug pool construction

python ./scripts/utils/build_drugset.py --input FDA_drugset.xlsx --output FDA_drugset.pickle

The command facilitates the construction of a drug set by processing an input Excel file and outputing a pickle file for use in cell model environments.

Parameters

  • input: the file name of an Excel file containing the compound information of the drug set. If users wish to create a custom drug set, they must store thier input file in ./data/ and ensure that the input Excel file contains the following five required columns.
    1. Index: The index of the compound.
    2. Name: The name of the compound.
    3. Target: The target(s) of the compound. Multiple targets should be separated by commas. If the target is unknown, use "Unknown".
    4. Pathway: The pathway associated with the compound. If the pathway is unknown, use "Unknown".
    5. SMILES: The SMILES (Simplified Molecular Input Line Entry System) notation of the compound. Ensure that the correct SMILES notation is provided.
  • output: The output is the file name of a pickle file containing the processed compound information of the drug set in ./scripts/gym_cell_model/gym_cell_model/data directory.

1.2 Write configurations

python ./scripts/utils/write_scenario_configs.py --drugset_file_name FDA_drugset.pickle --drugset_short_name FDA --cell_line MCF7 --terminal_step 2

This command prepares configurations prior to running the RL agents by modifying two config files:

  • env_cpd.config: This file contains configurations for the cell model environment located in /scripts/gym_cell_model/gym_cell_model/config/. The command will append configurations named in the format ENV_[drugset_short_name]_[cell_line]_[terminal_step], like ENV_FDA_MCF7_2.
  • RL_agent.config: This file contains configurations for the RL agents located in /scripts/AlphaTherapy/config/. The command will append configurations named in the format ENV_[drugset_short_name]_[cell_line]_[terminal_step]_SEED[i], where i ranges from 1 to 10, like ENV_FDA_MCF7_2_SEED1.

Parameters

  • drugset_file_name: the pickle file name of drug set which saved in the above command.
  • drugset_short_name: a phrase or a short name for easy reference of the drug set.
  • cell_line: the cell line you want to research.
  • terminal_step: a number of the terminal step you want to reearch.

Notably, we only add the information of the scenario to RL_agents.config. You also can modify the model training parameters in RL_agents.config.

1.3 Run RL agents

nohup python ./scripts/AlphaTherapy/model/train_RL_agents.py --drugset_short_name FDA --cell_line MCF7 --terminal_step 2 --max_workers 10 &

This command trains RL agents through interaction with the cell model. It will generate policy.pth, plot_train_data.csv and plot_test_data.csv files within new directories like "./scripts/AlphaTherapy/working_log/ENV_FDA_MCF7_STEP2_SEED1". These files serve as records for model training, capturing all generated .pth files during runtime and the reward changes during training process.

Parameters

  • drugset_short_name, cell_line, terminal_step is the same as above.
  • max_workers: maximum number of available CPUs.

1.4 Merge results

python ./scripts/AlphaTherapy/model/output_result.py --drugset_short_name FDA --cell_line MCF7 --terminal_step 2

This command will organize the sequential drug combination results for each scenario, like ENV_FDA_A375_STEP2, and finally output a file named ENV_FDA_A375_STEP2.csv, stored in /output/AlphaTherapy/.

Parameters

  • drugset_short_name, cell_line, terminal_step is the same as above.

2. Downstream analysis

python ./scripts/downstream_analysis/src/run_downstram_analysis.py --combos_file_name ENV_FDA_MCF7_STEP2.csv --env_name ENV_FDA_MCF7_STEP2 --drugA_name "Morin Hydrate" --drugB_name "Afatinib (BIBW2992)"

This command will run downstream analysis and save the results in file like in /output/downstream_analysis/.

Parameters

  • combos_file_name: the name of file generated in the previous command like ENV_FDA_MCF7_STEP2.csv.
  • env_name: the config name of cell model, like ENV_FDA_A375_STEP2.
  • drugA_name: the name of the first frug from drug combos in the combos_file_name.csv.
  • drugB_name: the name of the second frug from drug combos in the combos_file_name.csv.

Citation

Contacts

bm2-lab@tongji.edu.cn

xiaohanchen@tongji.edu.cn