Solvation Meta Predictor

This repository contains code for predicting the aqueous solubility of organic molecules using machine learning models. The models and dataset are based on the research paper: Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations.

Usage

# pull the original PNNL codebase
git submodule init
git submodule update

# make the patch set
# this is not necessary since 2/19/2025 as the patch set is
#./make_patches

# apply the patch set
# preserved in the patches directory
./apply_patches

1. downlaod data as old instructions below

Old Usage

1. Download Data: Download the dataset from this link and save it as data.csv in the ./data folder.

2. Generate Features:

   • Generate Pybel coordinates and Molecular Dynamics (MDM) features by running create_data.py in the ./data folder:

     cd ./data
     python create_data.py

3. Train Models:

   • To train the MDM model, run train.py in the ./mdm folder:

     cd ../mdm
     python train.py

   • To train the GNN model, run train.py in the ./gnn folder:

     cd ../gnn
     python train.py

   • To train the SMI model, run train.py in the ./smi folder:

     cd ../smi
     python train.py

4. Make Predictions:

   • Use the predict.ipynb files in each model's folder to make predictions:

     cd ../mdm
     jupyter notebook predict.ipynb

     Repeat the above steps for the gnn and smi folders.

5. Ensemble Models:

   • To ensemble the models, run the following scripts:

     cd ../ensemble
     python CV.py
     python Optuna.py
     python KNN.py

6. Compare Predictions:

   • To compare predictions from individual models with ensemble methods, use the ensemble_prediction.ipynb notebook:

     jupyter notebook ensemble_prediction.ipynb

Solvation Meta Predictor Perfomacne

Additional Information

For detailed instructions on how to run the models, featurize the data, and other specifics, please refer to the original research paper linked above. The methods and techniques described in the paper are critical for understanding and effectively using this repository.