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Bayesgrad

BayesGrad: Explaining Predictions of Graph Convolutional Networks

The paper is available on arXiv, https://arxiv.org/abs/1807.01985.

Citation

If you find our work useful in your research, please consider citing:

@article{akita2018bayesgrad,
  title={BayesGrad: Explaining Predictions of Graph Convolutional Networks},
  author={Akita, Hirotaka and Nakago, Kosuke and Komatsu, Tomoki and Sugawara, Yohei and Maeda, Shin-ichi and Baba, Yukino and Kashima, Hisashi},
  journal={arXiv preprint arXiv:1807.01985},
  year={2018}
}

Setup

Chainer Chemistry [1] is used in our code. It is an extension library for deep learning framework Chainer [2], and it supports several graph-convolutional neural network together with chemical dataset management.

The experiment is executed under following environment:

• OS: Linux
• python: 3.6.1
• conda version: 4.4.4

conda create -n bayesgrad python=3.6.1
source activate bayesgrad
pip install chainer==4.2.0
pip install chainer-chemistry==0.4.0
conda install -c rdkit rdkit==2017.09.3.0
pip install matplotlib==2.2.2
pip install future==0.16.0
pip install cairosvg==2.1.3
pip install ipython==5.1.0

[Note] Please install specified python version & rdkit version. Latest python version and rdkit may not work well as discussed here. If you face error try

conda install libgcc

If you want to use GPU, please install cupy as well.

# XX should be CUDA version (80, 90 or 91)
pip install cupy-cudaXX==4.2.0

Experiments

Each experiment can be executed as follows.

Tox21 Pyridine experiment

Experiments described in Section 4.1 in the paper. Tox21 [3] dataset is used.

cd experiments/tox21

Training with all train data, plot precision-recall curve

Set -g -1 to use CPU, -g 0 to use GPU.

python train_tox21.py --iterator-type=balanced --label=pyridine --method=ggnndrop --epoch=50 --unit-num=16 --n-layers=1 -b 32 --conv-layers=4 --num-train=-1 --dropout-ratio=0.25 --out=results/ggnndrop_pyridine -g 0
python plot_precision_recall.py --dirpath=results/ggnndrop_pyridine

Visualization with trained model

See visualize-saliency-pyrigine.ipynb.

Our method successfully focuses on pyridine (C5H5N) substructures.

Training 30 different models with few train data, calculate RPC-AUC score

Argument: -1 to use CPU, 0 to use GPU.

Note that this experiment takes time (took around 2.5 hours with GPU in our environment), since it trains 30 different models.

bash -x ./train_few_with_seeds.sh 0
bash -x ./calc_prcauc_with_seeds.sh 0

Then see results/ggnndrop_pyridin_numtrain1000-seed0-29/prcauc_stats_absolute_0.15.csv for the results.

Tox21 SR-MMP experiment

Experiments described in Section 4.2 in the paper. Tox21 [3] dataset is used.

cd experiments/tox21

Training the model

Set -g -1 to use CPU, -g 0 to use GPU.

python train_tox21.py --iterator-type=balanced --label=SR-MMP --method=nfpdrop --epoch=200 --unit-num=16 --n-layers=1 -b 32 --conv-layers=4 --num-train=-1 --dropout-ratio=0.25 --out=results/nfpdrop_srmmp -g 0

Visualization of tox21 data & Tyrphostin 9 with trained model

See visualize-saliency-tox21.ipynb.

Jupyter notebook interactive visualization:

Several picked images:

Toxicity mechanism is still in an active research topic and it is difficult to quantitatively analyze its results. We hope these visualization helps to analyze and establish further knowledge about toxicity.

Solubility experiment

Experiment done in Section 4.3 in the paper. ESOL [4] dataset (provided by MoleculeNet [5]) is used.

cd experiments/delaney

Training the model

Set -g -1 to use CPU, -g 0 to use GPU.

python train.py -e 100 -n 3 --method=nfpdrop -g 0

Visualization with trained model

python plot.py --dirpath=./results/nfpdrop_M30_conv3_unit32_b32

Red color represents these atoms are hydrophilic, and blue color represents hydrophobic. Above figure is consistent with fundamental physicochemical knowledge as explained in the paper.

Saliency Calculation

Although only results of gradient method [6, 7, 8] are reported in the paper, this repository contains saliency calculation code for several other algorithms as well.

We can apply SmoothGrad [8] and/or BayesGrad (Ours) into following algorithms.

• Vanilla Gradients [6, 7]
• Integrated Gradients [9]
• Occlusion [10]

The code design is inspired by PAIR-code/saliency.

License

Our code is released under MIT License (see LICENSE file for details).

Reference

[1] pfnet research. chainer-chemistry https://github.com/pfnet-research/chainer-chemistry

[2] Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton. Chainer: a next-generation open source framework for deep learning. In Proceedings of Workshop on Machine Learning Systems (LearningSys) in Advances in Neural Information Processing System (NIPS) 28, 2015.

[3] Ruili Huang, Menghang Xia, Dac-Trung Nguyen, Tongan Zhao, Srilatha Sakamuru, Jinghua Zhao, Sampada A Shahane, Anna Rossoshek, and Anton Simeonov. Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Frontiers in Environmental Science, 3:85, 2016.

[4] John S. Delaney. Esol: Estimating aqueous solubility directly from molecular structure. Journal of Chemical Information and Computer Sciences, 44(3):1000{1005,2004. PMID: 15154768.

[5] Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, Vijay Pande, MoleculeNet: A Benchmark for Molecular Machine Learning, arXiv preprint, arXiv: 1703.00564, 2017.

[6] Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pascal Vincent. Visualizing Higher-Layer Features of a Deep Network. 2009.

[7] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classication models and saliency maps. arXiv preprint arXiv:1312.6034, 2013.

[8] Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viegas, and Martin Wattenberg. SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825, 2017.

[9] Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In Doina Precup and Yee Whye Teh (eds.), Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pp. 3319–3328, International Convention Centre, Sydney, Australia, 06–11 Aug 2017. PMLR. URL http://proceedings.mlr.press/v70/sundararajan17a.html.

[10] Matthew D Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In European conference on computer vision, pp. 818–833. Springer, 2014.