https://github.com/brianhie/icml18-jtnn
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)
Science Score: 10.0%
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Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)
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# Junction Tree Variational Autoencoder for Molecular Graph Generation Official implementation of our Junction Tree Variational Autoencoder [https://arxiv.org/abs/1802.04364](https://arxiv.org/abs/1802.04364) # Accelerated Version We have accelerated our code! The new code is in `fast_jtnn/`, and the VAE training script is in `fast_molvae/`. Please refer to `fast_molvae/README.md` for details. # Requirements * Linux (We only tested on Ubuntu) * RDKit (version >= 2017.09) * Python (version == 2.7) * PyTorch (version >= 0.2) To install RDKit, please follow the instructions here [http://www.rdkit.org/docs/Install.html](http://www.rdkit.org/docs/Install.html) We highly recommend you to use conda for package management. # Quick Start The following directories contains the most up-to-date implementations of our model: * `fast_jtnn/` contains codes for model implementation. * `fast_molvae/` contains codes for VAE training. Please refer to `fast_molvae/README.md` for details. The following directories provides scripts for the experiments in our original ICML paper: * `bo/` includes scripts for Bayesian optimization experiments. Please read `bo/README.md` for details. * `molvae/` includes scripts for training our VAE model only. Please read `molvae/README.md` for training our VAE model. * `molopt/` includes scripts for jointly training our VAE and property predictors. Please read `molopt/README.md` for details. * `jtnn/` contains codes for model formulation. # Contact Wengong Jin (wengong@csail.mit.edu)
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- Name: Brian Hie
- Login: brianhie
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- Location: San Francisco
- Website: brianhie.com
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- Repositories: 36
- Profile: https://github.com/brianhie