molprivacy 0.1.0

Assessing training data privacy for molecular property prediction.

Training data privacy assessment for molecular property prediction

Python package to assess how much information somebody can deduct from a neural network trained on some (confidential) training data for molecular property prediction.

Getting Started

These instructions will help you install the python package and conduct a privacy assessments on data for molecular property prediction.

Prerequisites

You need to have an environment with python version 3.12. This can be created for exampple using conda.

$ conda create -n privacy_env python=3.12 
$ conda activate privacy_env 

Installation

Package can be installed from PyPI with pip

$ pip install molprivacy

Usage

The package can be run via the command line or by importing the privacy_test function into your script.

Command line interface

You can run the privacy test directly from the command line using the privacytest command after installing the package.

$ privacytest --representation REPRESENTATION --result_folder RESULT_FOLDER [options]

Required Arguments

--representation: Specifies the molecular representation to use. Choices are:

• ECFP4
• ECFP6
• MACCS
• graph
• rdkit
• transformer_vector
• transformer_matrix

--result_folder: Path to the folder where the results will be stored.

Optional Arguments

--dataset: Specifies the dataset to use. Choices are:

• ames (default)
• herg
• del
• bbb
• file (use your own dataset; requires --dataset_path)

--dataset_path: Path(s) to your custom dataset file(s). Required if --dataset file is selected. The dataset must have a 'smiles' column and a binary 'label' column.

--split: Split ratios for training, validation, and testing datasets. Provide three float values that sum to 1.0. Testing dataset will be used for privacy assessment. Default: 0.45 0.1 0.45

--hyperparameter_optimization_time: Time in seconds allocated for hyperparameter optimization during model training. Default: 600

--attack_data_fraction: Fraction of data to use for the Reverse Model Inversion Attack (RMIA). Reduce this value if the RMIA attack runs out of memory. Default: 1.0

Examples

Run with default dataset and parameters:

$ privacytest --representation ECFP4 --result_folder "home/results"

Use a custom dataset:

$ privacytest --representation MACCS --result_folder "home/results" --dataset file --dataset_path "home/data/my_dataset.csv"

Help

For a full list of available options and detailed descriptions, run:

$ privacytest --help

Import into a script

In addition to running the privacy test from the command line, you can directly use the privacy_test function in your Python scripts. This allows for seamless integration into your workflows and the ability to to customize parameters programmatically.

Import the function

from privacytest.__main__ import privacy_test

Example usage

from privacytest.__main__ import privacy_test
# Import your custom representation function (needs to have a smiles string as input and a tuple of the encoding vector and the vectors dimension as an output)
from mycode import my_custom_representation_function

# Define privacy test parameters
representation = 'custom'
result_folder = 'home/results'
dataset = 'ames'
split = [0.45, 0.1, 0.45]
hyperparameter_optimization_time = 600
attack_data_fraction = 1.0
custom_representation_function = my_custom_representation_function

# Run the privacy test with custom representation
privacy_test(
    representation=representation,
    result_folder=result_folder,
    dataset=dataset,
    split=split,
    hyperparameter_optimization_time=hyperparameter_optimization_time,
    attack_data_fraction=attack_data_fraction,
    custom_representation_function=custom_representation_function
)

Privacy Test Output

When you run the privacy test, the results are saved in the result_folder you specified. The folder structure contains the following files and directories:

privacy/

• results/: This folder contains the outputs related to privacy performance for both the LiRA and RMIA privacy attacks.

  • lira/:

    • privacy_performance_overview.txt: A text file summarizing the results of the LiRA privacy attack.
    • privacy_performance.pdf: ROC curve for the LiRA attack (positives are training data samples).
    • ROC.csv: CSV file containing data of ROC curve.

  • rmia/:

    • privacy_performance_overview.txt: A text file summarizing the results of the RMIA privacy attack.
    • privacy_performance.pdf: ROC curve for the RMIA attack.
    • ROC.csv: CSV file containing data of ROC curve.

  • true_positives_at_FPR0/:

    • lira.csv: CSV file that contains all the chemical structures that LiRA could identify at a False Positive Rate of 0.
    • rmia.csv: CSV file that contains all the chemical structures that RMIA could identify at a False Positive Rate of 0.

model/

• model_performance.pdf: ROC curve of the model performance in the binary classification task.
• model.ckpt: The checkpoint file for the final model that was trained with the optimized hyperparameters.
• optimization.db: Database containing the optimization history during hyperparameter tuning.
• optimized_hyperparameters.yaml: A YAML file that contains the optimized hyperparameters after the search process.

data_dir/

• train.csv, validation.csv, test.csv: These files contain the datasets used for training, validation, and testing. Will vary when re-running the package since the split is random.

model_config.yaml

• A configuration file that summarizes all model related configurations.

privacy_config.yaml

• A configuration file that summarizes all membership inference attack related configurations.

Citation

This repository is part of the paper "Publishing neural networks in drug discovery compromises training data privacy". \ Pre-print: TODO \ Bibtex: TODO