PyTorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules" by Gómez-Bombarelli, et al.
Link to Paper - arXiv
To clone the repo on your machine run -
git clone https://github.com/Ishan-Kumar2/Molecular_VAE_Pytorch.git
The Structure of the Repo is as follows -
data_prep.py- For Getting the Data in CSV format and splitting into specifed sized Train and Val
main.py - Running the model
model.py - Defines the Architecture of the Model
utils.py - Various useful functions for encoding and decoding the data
For this work I have used the ChEMBL Dataset which can be found here
Since the whole dataset has over 16M datapoints, I have decided to use a subset of that data.
To get the subset you can either use the train, val data present in /data
or run the data_prep.py file as -
python data_prep.py /path/to/downloaded_data col_name_smiles /save/path 50000
This will prepare 2 CSV files /save/path_train.csv and /save/path_val.csv both of length 50k and having randomly shuffled datapoints.
Example of a Smiles string and corresponding Molecule
To train the network use the main.py file
To Run the Papers Model (Conv Encoder and GRU Decoder)
python main.py ./data/chembl_500k_train ./data/chembl_500k_val ./Save_Models/ --epochs 100 --model_type mol_vae --latent_dim 290 --batch_size 512 --lr 0.0001
Latent Dim has default value 292 which is the value used in the original Paper
To Run a VAE with Fully Connected layers in both Encoder Decoder
python main.py ./data/bbbp.csv ./Save_Models/ --epochs 1 --model_type fc --latent_dim 100 --batch_size 20 --lr 0.0001
The Train and Validation Losses where tracked for Training and Validation epochs
Using Latent Dim = 292 (As in the Paper)
It starts to overfit the train set after 20 Epochs, so the saved weights at 20 should be used for best results
Although the Training Loss Reduces more in the 392 Case the Validation Loss remains almost equal which means it starts to overfit after 292.
Input - \CC(C)(C)C(=O)OCN1OC(=O)c2ccccc12
Output - \CC(C)CC)C(=O)OC11CC(=O)C2ccccc12
Input - \CN\C(=N\S(=O)(=O)c1cc(CCNC(=O)c2cc(Cl)ccc2OC)ccc1OCCOC)\S
Output - \CN\C(=N/S)=O)(=O)c1ccccCNC(=O)c2cc(Cl)ccc2OC)ccc1OCC(C(\C
Input - \O[C@@H]1C@@HC@@HN(CCCCCNC(=O)c3ccccc3)C(=O)N(CCCCCNC(=O)c4ccccc4)[C@@H]1Cc5ccccc5
Output - \O[C@@H]1C@@HC@@HN(CcCCCN3(=O)c3ccccc3)C(=O)N4Cc44NC4C=O)c4cccc54)c1Cc5ccccc5
Input - \C\C(=N/OC(c1ccccc1)c2ccccc2)\C[C@H]3CCc4c(C3)cccc4OCC(=O)O
Output - \C\C(=N/OC(c1ccccc1)\2ccccc2)\C33CNC4ccc))ccc44OCC=O)O
Input - \OC@@Hc5cccnc5
Output- \OC@@Hcc1)c5cccnc5
Input- \CCCCCCCCCCc1cccc(O)c1C(=O)O
Output- \CCCCCCCCCCc1ccccccc)CC(O))O