This is the code of paper De Novo Molecular Generation via Connection-aware Motif Mining. Zijie Geng, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Jie Wang, Yongdong Zhang, Feng Wu, Tie-Yan Liu. ICLR 2023. [arXiv]
- Python 3.7
- Pytorch
- rdkit
- networkx
- torch-geometric
- guacamol
Put the dataset under the ./data directory. Name the training set and avlid set as train.smiles and valid.smiles, respectively. An example of the working directory is as following.
AI4Sci-MiCaM
├── data
│ └── QM9
│ ├── train.smiles
│ └── valid.smiles
├── output/
├── preprocess/
├── src/
└── README.md
It consists of two phases: merging operation learning and motif vocabulary construction.
For merging operation learning, run the commands in form of
python src/merging_operation_learning.py \
--dataset QM9 \
--num_workers 60
For motif vocabulary constraction, run the commands in form of
python src/motif_vocab_construction.py \
--dataset QM9 \
--num_operations 1000 \
--num_workers 60
To generate training data, using a given motif vocabulary, run the commands in form of
python src/make_training_data.py \
--dataset QM9 \
--num_operations 1000 \
--num_workers 60
Alternatively, to run the entire preprocessing workflow, which includes mining motifs and generating training data, just run the commands in form of
python src/preprocess.py \
--dataset QM9 \
--num_operations 1000 \
--num_workers 60
To train the MiCaM model, run a command in form of
python src/train.py \
--job_name train_micam \
--dataset QM9 \
--num_operations 1000 \
--batch_size 2000 \
--depth 15 \
--motif_depth 6 \
--latent_size 64 \
--hidden_size 256 \
--dropout 0.3 \
--steps 30000 \
--lr 0.005 \
--lr_anneal_iter 50 \
--lr_anneal_rate 0.99 \
--beta_warmup 3000 \
--beta_min 0.001 \
--beta_max 0.3 \
--beta_anneal_period 40000 \
--prop_weight 0.2 \
--cuda 0
python src/train.py \
--job_name train_zinc \
--dataset zinc \
--num_operations 500 \
--batch_size 500 \
--depth 15 \
--motif_depth 6 \
--latent_size 256 \
--hidden_size 256 \
--dropout 0.1 \
--steps 60000 \
--lr 0.005 \
--lr_anneal_iter 100 \
--lr_anneal_rate 0.995 \
--beta_warmup 10000 \
--beta_min 0.001 \
--beta_max 0.7 \
--beta_anneal_period 100000 \
--prop_weight 0.2 \
--cuda 0
python src/train.py \
--job_name train_guacamol \
--dataset guacamol \
--num_operations 500 \
--batch_size 500 \
--depth 15 \
--motif_depth 6 \
--latent_size 256 \
--hidden_size 256 \
--dropout 0.1 \
--steps 60000 \
--lr 0.001 \
--lr_anneal_iter 100 \
--lr_anneal_rate 0.995 \
--beta_warmup 10000 \
--beta_min 0.001 \
--beta_max 0.6 \
--beta_anneal_period 100000 \
--prop_weight 0.2 \
--cuda 0
Benchmarking will be automatically conduct during the training process.
If you find this code useful, please consider citing the following paper.
@inproceedings{
geng2023de,
title={De Novo Molecular Generation via Connection-aware Motif Mining},
author={Zijie Geng and Shufang Xie and Yingce Xia and Lijun Wu and Tao Qin and Jie Wang and Yongdong Zhang and Feng Wu and Tie-Yan Liu},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=Q_Jexl8-qDi}
}
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