eskape-model 1.0.2

The ESKAPE Model is a machine learning-based online resource to facilitate discovery of novel antibiotics against the ESKAPE pathogens.

The ESKAPE Model Standalone

This repository provides a standalone application to the web version of ESKAPE Model at eskape.mcmaster.ca. The ESKAPE Model is a machine learning-based online resource to facilitate discovery of novel antibiotics against the ESKAPE pathogens, a group of multidrug-resistant bacteria that are responsible for the majority of hospital-acquired infections.

The ESKAPE Model predicts the antibacterial activity of inputted molecules against each of the following ESKAPE pathogens:

• EF - Enterococcus faecium
• SA - Staphylococcus aureus
• KP - Klebsiella pneumoniae
• AB - Acinetobacter baumannii
• PA - Pseudomonas aeruginosa
• BW - Escherichia coli (wildtype)
• DKO - Escherichia coli (hyperpermeable and efflux deficient)

Models were trained on in-house growth inhibition screening datasets against common laboratory strains of each pathogen. A total of 21 models were trained - three model architectures for each pathogen:

• Random forest using Morgan fingerprints
• Chemprop graph neural network
• Chemprop with RDKit features

Installation

The tool requires Python 3.10.

Create a virtual environment

python3 -m venv eskape_env
source eskape_env/bin/activate

Install eskape_model using pip

The latest release can be installed directly from pip or this repository which will also install the dependencies chemprop and chemfunc.

pip install eskape_model

Or

Install eskape_model using tarball

Install the eskape_model application within the created eskape_model python environment using a tarball.

(eskape_env) amos@Amogelangs-MacBook-Pro % python3 -m pip install /path/to/eskape_model-1.0.0.tar.gz

Dependencies

The following are required dependencies (listed below):

• chemprop version 1.6.1 - https://github.com/chemprop/chemprop.git
• chemfunc version 1.0.10 - https://github.com/swansonk14/chemfunc.git

Install dependencies

install chemprop v1.6.1

wget https://github.com/chemprop/chemprop/archive/refs/tags/v1.6.1.tar.gz
python3 -m pip install v1.6.1.tar.gz

install chemfunc v_1.0.10

wget https://github.com/swansonk14/chemfunc/archive/refs/tags/v_1.0.10.tar.gz
python3 -m pip install v_1.0.10.tar.gz

install specific scikit-learn and numpy

(eskape_env) amos@Amogelangs-MacBook-Pro % pip install scikit-learn==1.3.2
(eskape_env) amos@Amogelangs-MacBook-Pro % pip install numpy==1.26.4

test functions

(eskape_env) amos@Amogelangs-MacBook-Pro % chemprop_predict -h
(eskape_env) amos@Amogelangs-MacBook-Pro % sklearn_predict -h
(eskape_env) amos@Amogelangs-MacBook-Pro % chemfunc -h
(eskape_env) amos@Amogelangs-MacBook-Pro % eskape_model -h

Download ESKAPE model models from eskape.mcmaster.ca or GitHub

Please download the models and training data at GitHub.

Create a directory db with two sub-directories canonical_data and models. From the downloaded models data, add training_data_canonical.csv to db/canonical_data/ directory. Add all models to directory db/models/all/.

The tree structure of db should look like so:

(eskape_env) amos@Amogelangs-MacBook-Pro db % tree -L 3
.
├── canonical_data
│   └── training_data_canonical.csv
└── models
    └── all
        ├── AB_chemprop
        ├── AB_rdkit
        ├── AB_rf
        ├── BW_chemprop
        ├── BW_rdkit
        ├── BW_rf
        ├── DKO_chemprop
        ├── DKO_rdkit
        ├── DKO_rf
        ├── EF_chemprop
        ├── EF_rdkit
        ├── EF_rf
        ├── KP_chemprop
        ├── KP_rdkit
        ├── KP_rf
        ├── PA_chemprop
        ├── PA_rdkit
        ├── PA_rf
        ├── SA_chemprop
        ├── SA_rdkit
        └── SA_rf

run eskape_model

(eskape_env) amos@Amogelangs-MacBook-Pro % eskape_model \
--input_file input.txt \
--output_directory output \
--models_directory db \
--debug > run.log 2>&1 &