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Original implementation of the paper "SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery" by Shion Honda et al.
| Date | Stars |
|---|---|
| 2026-07-24 | 359 |
| 2026-07-25 | 359 |
| 2026-07-28 | 359 |
| 2026-07-30 | 359 |
| 2026-08-06 | 359 |
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# SMILES Transformer
[SMILES Transformer](http://arxiv.org/abs/1911.04738) extracts molecular fingerprints from string representations of chemical molecules.
The transformer learns latent representation that is useful for various downstream tasks through autoencoding task.
## Requirement
This project requires the following libraries.
- NumPy
- Pandas
- PyTorch > 1.2
- tqdm
- RDKit
## Dataset
Canonical SMILES of 1.7 million molecules that have no more than 100 characters from Chembl24 dataset were used.
These canonical SMILES were transformed randomly every epoch with [SMILES-enumeration](https://github.com/EBjerrum/SMILES-enumeration) by E. J. Bjerrum.
## Pre-training
After preparing the SMILES corpus for pre-training, run:
```
$ python pretrain_trfm.py
```
Pre-trained model is [here](https://drive.google.com/file/d/1LwE2BzvtDaPGYv0OR6iBjmsqoloH885N/view?usp=sharing).
## Downstream Tasks
See `experiments/` for the example codes.
## Cite
```
@article{honda2019smiles,
title={SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery},
author={Shion Honda and Shoi Shi and Hiroki R. Ueda},
year={2019},
eprint={1911.04738},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
Excerpt of 1,249 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:699b045535ae3a93, topic:deep-learning, topic:pytorch, readme:pre-training
matched fp:699b045535ae3a93, topic:transformer
matched fp:699b045535ae3a93, topic:chemistry, desc:drug discovery, readme:drug discovery