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Sc2Mol: A Scaffold-based Two-step Molecule Generator with Variational Autoencoder and Transformer

Recent update

2024-01-10: Fix a bug in transformer.py, which may cause error when get_config method is called.
2023-09-11: Code is modified to support batch random generation. Much more faster!

Requirements

python 3.7
tensorflow-gpu >= 2.3.0
rdkit >= 2020.03.2.0
pandas >= 1.0.3
numpy >= 1.19.2

Data

See data fold to get data and preprocess data.

Training

python training.py --input_npy ./data/training_raw_chain --target_npy ./data/training_raw_target --npy_num 10
More arguments setting can be found and changed in code file training.py.

Evaluating (random generation)

python eval_random.py --num_sample NUM_SAMPLE --output OUTPUT --ckpt CHECKPOINT
More arguments setting can be found and changed in code file eval_random.py.

Evaluating (given scaffold)

python eval_from_scaffold.py --input INPUT_NPY --target TARGET_NPY --num_sample NUM_SAMPLE --output OUTPUT_FILE --ckpt CHECKPOINT_PATH
More arguments setting can be found and changed in code file eval_from_scaffold.py.

Citation

Zhirui Liao, Lei Xie, Hiroshi Mamitsuka, Shanfeng Zhu, Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer, Bioinformatics, Volume 39, Issue 1, January 2023, btac814, https://doi.org/10.1093/bioinformatics/btac814

Contacts and bug reports

Please feel free to send bug reports or questions to Zhirui Liao: zrliao19@fudan.edu.cn and Prof. Shanfeng Zhu: zhusf@fudan.edu.cn

Declaration

It is free for non-commercial use. For commercial use, please contact with Zhirui Liao and Prof. Shanfeng Zhu (zhusf@fudan.edu.cn).