Home

Awesome

GraphINVENT-CNS

An implementation of the GraphINVENT framework using curated data of CNS molecules. The framework was optimized for the drug discovery of Parkinson's disease.

Prerequisites

References

This work is based on GraphINVENT framework:

https://github.com/MolecularAI/GraphINVENT

@article{mercado2020graph,
  author = "Rocío Mercado and Tobias Rastemo and Edvard Lindelöf and Günter Klambauer and Ola Engkvist and Hongming Chen and Esben Jannik Bjerrum",
  title = "{Graph Networks for Molecular Design}",
  journal = {Machine Learning: Science and Technology},
  year = {2020},
  publisher = {IOP Publishing},
  doi = "10.1088/2632-2153/abcf91"
}

@article{mercado2020practical,
  author = "Rocío Mercado and Tobias Rastemo and Edvard Lindelöf and Günter Klambauer and Ola Engkvist and Hongming Chen and Esben Jannik Bjerrum",
  title = "{Practical Notes on Building Molecular Graph Generative Models}",
  journal = {Applied AI Letters},
  year = {2020},
  publisher = {Wiley Online Library},
  doi = "10.1002/ail2.18"
}

It also makes use of the following filters/benchmarkers to test resulting molecules:

The weights of the embedding layer in the LSTM QSAR model were initialized to the weights of the pretrained word2vec model "Mol2Vec": https://github.com/samoturk/mol2vec

Related work

MPNNs

The MPNN implementations used in this work were pulled from Edvard Lindelöf's repo in October 2018, while he was a masters student in the MAI group. This work is available at https://github.com/edvardlindelof/graph-neural-networks-for-drug-discovery.

His master's thesis, describing the EMN implementation, can be found at

https://odr.chalmers.se/handle/20.500.12380/256629.

RL-GraphINVENT https://github.com/olsson-group/RL-GraphINVENT and(https://doi.org/10.33774/chemrxiv-2021-9w3tc).

Graph traversal algorithms in GraphINVENT (https://doi.org/10.33774/chemrxiv-2021-5c5l1)

License

GraphINVENT is licensed under the MIT license and is free and provided as-is.