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GraphFP: Fragment-based Pretraining and Finetuning on Molecular Graphs

NeurIPS 2023

Authors: Kha-Dinh Luong, Ambuj Singh

[Paper] [ArXiv] [Slides]

In this repository, we provide the code used in the NeurIPS 2023 paper Fragment-based Pretraining and Finetuning on Molecular Graphs. We also include the model weights to reproduce the results from the paper.

Environment

The source code is organized into 2 directories:

  • mol: experiments on small molecule prediction tasks, corresponding to Table 2 from the paper.
  • long-range: experiments on long-range datasets, corresponding to Table 3 from the paper.

The folders contain different chemical featurizations and GNN implementations. To ensure fair comparison with previous works, the mol folder contains codes compatible with those from Hu et al. (2019). On the other hand, long-range folder contains newer codes, with featurizations and GNN implementations provided in Open Graph Benchmark. As a result, the environment requirements for both scenarios are also different.

The package requirements for mol and long-range are in mol/requirements.txt and long-range/requirements.txt, respectively.

Data Preparation

We need to prepare data before either pretraining or finetuning. This process will create and store a molecular graph and a fragment graph for each molecule based on a vocabulary of fragments. We provide the vocabulary used in the paper in both mol/vocab.txt or long-range/vocab.txt. In long-range, to prepare datasets, run:

python prepare_data.py --root <output data path> --data_file_path <raw data path> --smiles_column <column containing smiles in raw file> --vocab_file_path vocab.txt

Similarly, in mol, run:

python prepare_data_old.py --root <output data path> --data_file_path <raw data path> --smiles_column <column containing smiles in raw file> --vocab_file_path vocab.txt

Parameters: root: where the processed data should be stored. data_file_path: path to the raw data file, which is expected to be in CSV. There should be 1 column containing SMILES string. The other columns contain task labels. smiles_column: name of the column containing SMILES string in the raw data file. vocab_file_path: path to the file containing the vocabulary of fragments.

Pretraining

To run pretraining on a prepared pretraining dataset, in mol, run:

python train_gnn_predictive_old.py --root <path to prepared data> --alpha <weight parameter> --save_path <path to save pretrained model>

To train with only the contrastive task, set alpha to 0. To train with both contrastive and predictive tasks, set alpha to between 0 and 1. To train with only predictive tasks, set alpha to 1. The dataset we prepared for pretraining can be accessed here. Unzip and specify root with the path to the unzipped folder.

Similary, in long-range, run:

python train_gnn.py --root <path to prepared data> --save_path <path to save pretrained model>

We only did contrastive pretraining on long-range benchmark so there is no alpha in this case.

Finetuning

To reproduce finetuning results, please follow the README in mol or long-range.

Citation

@inproceedings{
    2023fragmentbased,
    title={Fragment-based Pretraining and Finetuning on Molecular Graphs},
    author={Luong, Kha-Dinh and Singh, Ambuj},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023},
    url={https://openreview.net/forum?id=77Nq1KjmLl}
}