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Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast

Journal of Chemical Information and Modeling [Paper] [arXiv] [PDF] </br>

Yuyang Wang, Rishikesh Magar, Chen Liang, Amir Barati Farimani </br> Carnegie Mellon University </br>

This is the offical implementation of <strong><em>iMolCLR</em></strong>: "Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast". If you find our work useful in your research, please cite:

@article{wang2022improving,
  title={Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast},
  author={Wang, Yuyang and Magar, Rishikesh and Liang, Chen and Farimani, Amir Barati},
  journal={Journal of Chemical Information and Modeling},
  volume={59},
  number={8},
  pages={3370--3388},
  year={2022},
  publisher={ACS Publications},
  doi={10.1021/acs.jcim.2c00495}
}

@article{wang2022molclr,
  title={Molecular contrastive learning of representations via graph neural networks},
  author={Wang, Yuyang and Wang, Jianren and Cao, Zhonglin and Barati Farimani, Amir},
  journal={Nature Machine Intelligence},
  pages={1--9},
  year={2022},
  publisher={Nature Publishing Group},
  doi={10.1038/s42256-022-00447-x}
}

Getting Started

Installation

Set up conda environment and clone the github repo

# create a new environment
$ conda create --name imolclr python=3.7
$ conda activate imolclr

# install requirements
$ pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 -f https://download.pytorch.org/whl/torch_stable.html
$ pip install torch-geometric==1.6.3 torch-sparse==0.6.9 torch-scatter==2.0.6 -f https://pytorch-geometric.com/whl/torch-1.7.0+cu110.html
$ pip install PyYAML
$ conda install -c conda-forge rdkit=2021.09.1 
$ conda install -c conda-forge tensorboard

# clone the source code of iMolCLR
$ git clone https://github.com/yuyangw/iMolCLR.git
$ cd iMolCLR

Dataset

You can download the pre-training data and benchmarks used in the paper here and extract the zip file under ./data folder. The data for pre-training can be found in pubchem-10m-clean.txt. All the databases for fine-tuning are saved in the folder under the benchmark name. You can also find the benchmarks from MoleculeNet.

Pre-training

To train the iMolCLR, where the configurations are defined in config.yaml

$ python imolclr.py

To monitor the training via tensorboard, run tensorboard --logdir ckpt/{PATH} and click the URL http://127.0.0.1:6006/.

Fine-tuning

To fine-tune the iMolCLR pre-trained model on downstream molecular benchmarks, where the configurations are defined in config_finetune.yaml

$ python finetune.py

Pre-trained model

We also provide a pre-trained model, which can be found in ckpt/pretrained. You can load the model by change the fine_tune_from variable in config_finetune.yaml to pretrained.