This is the official Implementation of ICLR 2022 submission *Stepping Back to SMILES Transformers for Fast Molecular Representation Inference*.

##### Requirements

Please use `requirements.txt` in this directory to create a conda virtual environment to run our code.

##### PCQM4M-LSC Knowledge Distillation

To reproduce this experiment, please do the following:

1. Follow <https://github.com/microsoft/Graphormer> to reproduce the training of Graphormer-Large on PCQM4M-LSC, and save the model checkpoint with best performance.
2. Place the Graphormer checkpoint under folder `saved_checkpoints`,  and rename it to `graphormer_final.ckpt`.
3. Under folder `exp_scripts`, run `bash ogb_lsc_train.sh`.

##### Molecular Property Prediction

To reproduce these experiments, please do the following:

1. Find the ST-KD model checkpoint with best performance in the previous distillation experiment,place it under folder `saved_checkpoints.ckpt`, and rename it to `STKD_final.ckpt`,
2. Under folder `exp_scripts`, run `bash ${dataset}_lsc_train.sh`, where `dataset` can be `qm9`, `qm8`, `qm7` and `freesolv`.





