Code for paper
Wang, Sheng, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang. "SMILES-BERT: large scale unsupervised pre-training for molecular property prediction." In Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 429-436. 2019.
Note This code was developed with fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The fairseq version that we used in our code was around early 2019. Let us know if there is any license concern.
Note There are many unrated files/code to this paper and it could be hard to read and use the code. Following will be some commands we used for training, do not hesitate to reach out if you have any question.
A binarized dataset could speed up the dataset loading process. Here is the command:
python binarize_smiles.py --data /path/to/zinc --destdir /path/to/bin/zinc --workers 16
The dataset used for pretraining should contain three files train, valid, test and each of the file should be one SMILEs in one line, without header.
python train.py /path/to/pretrain/data-bin --data-bin --arch bertsmall --save-dir=/path/to/save/ckpts --task bert --max-sentences=256 --bert-pretrain True --optimizer adam --lr 0.0001 --adam-betas '(0.9, 0.999)' --weight-decay 0.01 --lr-scheduler inverse_sqrt --warmup-init-lr 1e-09 --warmup-updates 10000
A sample fine-tuning command could be
python train.py /path/to/labeled/data --arch bertsmall --task smile_property_prediction --save-dir /path/to/save/ckpts --max-sentences 16 --optimizer adam --lr 0.000001 --min-lr 1e-10 --adam-betas '(0.9, 0.999)' --weight-decay 0.01 --dropout 0.5 --lr-scheduler fixed --reverse-input False --pad-go True --left-pad-source=False --input-feed False --criterion seq3seq --prop-pred --num-props 1 --cls-index=[0] --pred-hidden-dim 0 --reset-optimizer --max-epoch=100
The dataset used for fine-tuning should contain three files train, valid, test and each of the file should be one SMILEs and properties separated by comma.
Our pre-trained model will be uploaded soon and the link will be updated here.
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