Hyunseung-Kim / molGCT

Paper Info.

Hyunseung Kim, Jonggeol Na*, and Won Bo Lee*, "Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention, " J. Chem. Inf. Model. 2021, 61, 12, 5804-5814
[Featured as JCIM journal supplentary cover] https://pubs.acs.org/doi/abs/10.1021/acs.jcim.1c01289

Generative Chemical Transformer (GCT) directly designs hit-like materials matched given target properties. Transformer's high level of context-awareness ability of chemical language aids in designing molecules more realistic and vaild.

Citation

@article{kim2021generative,
  title={Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention},
  author={Kim, Hyunseung and Na, Jonggeol and Lee, Won Bo},
  journal={Journal of Chemical Information and Modeling},
  volume={61},
  number={12},
  pages={5804--5814},
  year={2021},
  publisher={ACS Publications}
}

molGCT: Transformer+cVAE

Overview of GCT: Tranining phase and Inference phase

Design hit-like materials with 10-sets (#1~#10) of given target properties

Attention mechanism aids in undertanding semantically related parts of molecular structure hidden in 1-dimensional chemical language

GCT generates more realistic and valid based on the high level of chemical language comprehension of Transformer:
The molecules in circle are real molecules in MOSES database, and the molecules outside the circle are designed by GCT

Result examples

Implementation

This code was tested in Windows OS

1. Set up your anaconda environment with the following code:

conda env create -f molgct_env.yaml

2. Run to train GCT:

python train.py

3. Run to infer molecules with the trained GCT:

python inference.py

References

Basic Transformer code was borrowed and modified from: https://github.com/SamLynnEvans/Transformer
Molecular data were borrowed from: https://github.com/molecularsets/moses/tree/master/data