Offtarget_drugsafety

code for "In Silico Prediction of Drug Off-Target Profiles for Enhanced Drug Safety Assessment"

Below are the instructions for training models, predicting off-target profiles, applying these predictions, and setting up the required environment.

Train off-target profile prediction model

To train the off-target profile prediction model, execute the following script:

bash ./drugsafety/train.sh

This script retrains the model for seven types of targets. The trained model parameters are stored in the './drugsafety/results/data_combine_train' folder. Here we used the kinases dataset to demonstrate the training process of our model.

Predict off-target profile of compounds

To use the trained model for predicting off-target profiles of compounds, run:

python ./drugsafety/predict.py

The result will be save in './drugsafety/predict/predict_result' folder.

Apply the off-target profile prediction results

The predicted off-target profiles can be employed as molecular representations for the subsequent classification of a drug's ATC, toxicity, as well as ADR enrichment analysis.

ATC classification

We processed the collected ATC data into multi-label format in the './offtarget_application/ATC_classification/data_pro.ipynb' file, took the off-target predicted results of ATC related compounds as compound features, and ran the following code to train the ATC classification model.

python ./offtarget_application/ATC_classification/model/mlknn_offtarget.py

Toxicity prediction

We processed the toxic-related data in the './offtarget_application/Toxicity_prediction/toxic_data_pro.ipynb' file, took the off-target predicted results as compound features, and ran the '.ipynb' files under the './offtarget_application/Toxicity_prediction/model ' folder to train the different toxicity prediction model.

ADR enrichment analysis

According to the off-target panel prediction result of a compound, we obtain the interaction off-targets (gene_list), input the data into './offtarget_application/ADR_enrichment_analysis/enrich_code_drugs.ipynb' file, and run the file to obtain the ADR enrichment analysis results of the compound.

Setup and dependencies

The project environment requirements are listed in 'requirements.txt'.

Requirements

python = 3.8.0
pytorch = 1.11.0
deepchem = 2.6.1
dgllife = 0.2.9
scikit-learn = 0.24.2
numpy = 1.22.1
pandas = 1.4.2
rdkit = 2022.03.2
gseapy = 1.1.0