HPE

Heterogeneous Predictors Ensembling for Quantitative Toxicity Prediction

Article Link

https://www.researchsquare.com/article/b7fb29ea-0788-4bb1-bc65-3bbf9004b040/v1

System Setting:

Our OS is Ubuntu 18.04.3 LTS. We will build a system or a virtual envoirnment using conda for our HPE model development. Please follow the procedure below in the link to set up conda envoirnment.

https://www.digitalocean.com/community/tutorials/how-to-install-anaconda-on-ubuntu-18-04-quickstart

Copy the file lib.sh in your working directory where you have activated conda envoirnment. Run the following command. bash lib.sh
This will install all the required libraries. Pleas select "Yes" when asked during libraries installation process.

Following are the specific versions for various packges mentioned in lib.sh.

• tensorflow 1.13.1
• Keras 2.2.4
• scikit-learn 0.21.3
• pandas 0.25.0
• numpy 1.16.4
• scipy 1.2.1
• openpyxl 2.6.2
• xlsxwriter 1.1.8
• matplotlib 3.1.0
• rdkit 2018.09.1
• mordred 1.2.0
• padelpy 0.1.6
• deepchem 2.2.1.dev54
• xlrd 1.2.0

FCPC:

Create a folder with a name FCPC in your local machine. Download the following files from our GitHub FCPC folder into your local FCPC folder.

1. FCPC.py ## This is the code file to train and test the model.

2. train.csv ## This contains Activity column and SMILES strings column for train set.

3. test.csv ## This contains Activity column and SMILES strings column for test set.

cd into FCPC local directory while keeping the conda envoirnment activated. Now run the following command which will train the FCPC predictor on given train data. It will also generate 2D features files and .smi files for train and test both.

python FCPC.py

After the training and testing is done, a file with a name test_preds_FCPC.xlsx will be generated which contains output for the test data using FCPC predictor. It also generates two other files with names train_output.csv and test_output.csv which contains the 2D physio-chemical features used in training FCPC predictor.

FCPCe:

Create a folder with a name FCPCe in your local machine. Download the following files from our GitHub FCPCe folder into your local FCPCe folder.

1. FCPCe.py ## This is the code file to train and test the model.
2. train.csv ## This contains Activity column and SMILES strings column for train set.
3. test.csv ## This contains Activity column and SMILES strings column for test set.

cd into FCPCe local directory while keeping the conda envoirnment activated. Now run the following command which will train the FCPCe predictor on given train data.

python FCPCe.py

After the training and testing is done, a file with a name test_preds_FCPCe.xlsx will be generated which contains output for the test data using FCPCe predictor.

C1DS:

Create a folder with a name C1DS in your local machine. Download the following files from our GitHub C1DS folder into your local C1DS folder.

1. C1DS.py ## This is the code file to train and test the model.
2. train.xlsx ## This contains Activity column and SMILES strings column for train set.
3. test.xlsx ## This contains Activity column and SMILES strings column for test set.

cd into C1DS local directory while keeping the conda envoirnment activated. Now run the following command which will train the C1DS predictor on given train data.

python C1DS.py

After the training and testing is done, a file with a name test_preds_C1DS.xlsx will be generated which contains output for the test data using C1DS predictor.

C2DF:

Create a folder with a name C2DF in your local machine. Download the following files from our GitHub C2DF folder into your local C2DF folder.

1. C2DF.py ## This is the code file to train and test the model.
2. train.csv ## This contains Activity column and SMILES strings column for train set.
3. test.csv ## This contains Activity column and SMILES strings column for test set.

cd into C2DF local directory while keeping the conda envoirnment activated. Now run the following command which will train the C2DF predictor on given train data.

python C2DF.py

After the training and testing is done, a file with a name test_preds_C2DF.xlsx will be generated which contains output for the test data using C2DF predictor.

MGC:

Create a folder with a name MGC in your local machine. Download the following files from our GitHub MGC folder into your local MGC folder.

1. MGC.py ## This is the code file to train and test the model.
2. train.csv ## This contains Activity column and SMILES strings column for train set.
3. test.csv ## This contains Activity column and SMILES strings column for test set.

cd into MGC local directory while keeping the conda envoirnment activated. Now run the following command which will train the MGC predictor on given train data.

python MGC.py

After the training and testing is done, a file with a name test_preds_MGC.xlsx will be generated which contains output for the test data using MGC predictor.

MWC:

Create a folder with a name MWC in your local machine. Download the following files from our GitHub MWC folder into your local MWC folder.

1. MWC.py ## This is the code file to train and test the model.
2. train.csv ## This contains Activity column and SMILES strings column for train set.
3. test.csv ## This contains Activity column and SMILES strings column for test set.

cd into MWC local directory while keeping the conda envoirnment activated. Now run the following command which will train the MWC predictor on given train data.

python MWC.py

After the training and testing is done, a file with a name test_preds_MWC.xlsx will be generated which contains output for the test data using MWC predictor.

HPE:

NOw the final model by ensembling the outputs of all individual six predictors. Create a folder with a name HPE in your local machine. Download the HPE.py file from our GitHub HPE folder into your local HPE folder. Also copy the following files into HPE folder as well.

1. test_preds_FCPC.xlsx ## From FCPC local folder.
2. test_preds_FCPCe.xlsx ## From FCPCe local folder.
3. test_preds_C1DS.xlsx ## From C1DS local folder.
4. test_preds_C2DF.xlsx ## From C2DF local folder.
5. test_preds_MGC.xlsx ## From MGC local folder.
6. test_preds_MWC.xlsx ## From MWC local folder.
7. test.xlsx ## From C1DS local folder.This file contains the real activity values for test set.

cd into HPE local directory while keeping the conda envoirnment activated. Now run the following command which print the Accuracies on the screen after ensembling the indvidual predictors outputs.

python HPE.py