Code for the models in the paper: Predicting Aqueous Solubility of Organic Molecules Using Deep Learning Models with Varied Molecular Representations (https://pubs.acs.org/doi/full/10.1021/acsomega.2c00642)

Usage

1. Download data from https://figshare.com/s/542fb80e65742746603c and save it as data.csv in the ./data folder
2. Generate Pybel coordinates and MDM features by running create_data.py at the ./data folder
3. To train MDM, GNN, SMI and SCH models run train.py at ./mdm, ./gnn, ./smi and ./sch folderes respectively.
4. To make predictions use predict.ipynb files at each model folders.

Best models used to obtain the results in the paper are included in each model folders (mdm_paper.h5, gnn_paper.pt, smi_paper.h5, sch_paper.pt)

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