https://github.com/pfnet-research/step-wise-chemical-synthesis-prediction

A GGNN-GWM based step-wise framework for Chemical Synthesis Prediction
https://github.com/pfnet-research/step-wise-chemical-synthesis-prediction

chainer chemical-reactions deep-learning graph-convolutional-networks graph-neural-networks python

Last synced: 10 days ago
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A GGNN-GWM based step-wise framework for Chemical Synthesis Prediction

• Host: GitHub
• URL: https://github.com/pfnet-research/step-wise-chemical-synthesis-prediction
• Owner: pfnet-research
• License: mit
• Created: 2019-10-01T08:35:06.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-01T09:55:00.000Z (over 5 years ago)
• Last Synced: 2025-03-27T01:51:27.630Z (27 days ago)
• Topics: chainer, chemical-reactions, deep-learning, graph-convolutional-networks, graph-neural-networks, python
• Language: Python
• Homepage:
• Size: 399 KB
• Stars: 20
• Watchers: 13
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# A GGNN-GWM based step-wise framework for Chemical Synthesis Prediction

## Proposal

![](figure/framework.png)

The proposed framework contains 4 parts: reacted atom selection, reacted atom pair selection, bond type prediction, stop signal prediction. The reacted atom selection will execute for once, while other 3 parts will execute step-wisely. During each step, only top-1 pair will be selected and the bond type of the selected pair will be predicted. 

The whole step-wise framework was trained end-to-end.

Here are one failed example and one successed example to explain the current step-wise top1 inference metrics:

![](figure/example1.png)

In this example, reaction stops at step3. Step1 and step3 are same as ground truth but step2 is not, thus this inference is not correct.

![](figure/example2.png)

In this example, reaction stops at step3. All predicted steps are included in the ground truth regardless of the order, thus this inference is correct.

## Installation

```bash

pip install cupy==6.2.0

pip install chainer==6.2.0

pip install mpi4py

git clone https://github.com/pfnet-research/chainer-chemistry.git && pip install -e chainer-chemistry --no-dependencies

conda install -c rdkit rdkit==2017.09.3.0

```

## Dataset

We used dataset from [https://github.com/connorcoley/rexgen_direct/tree/master/rexgen_direct/data](https://github.com/connorcoley/rexgen_direct/tree/master/rexgen_direct/data)

use train.txt.proc.tar.gz for training

use test.txt.proc.tar.gz for validation and for inference

unzip them to folder 'dataset'

## Usage

```

# use USPTO-400k (all reactions in train.txt.proc) for training:

mpiexec -n 8 python train_framework.py --size 'all' --gwm true --out 'result_all' --pairlr 1e-3 --lr 1e-4 --decay_iter 40000 --batch_size 20 --epoch 300 --gpu --frequency 20 --hdim 50 --nn_hidden_dim 20 --n_layers 2 --concat_hidden false --weight_tying false

# use USPTO-15k (random 15k reactions in train.txt.proc) for training:

mpiexec -n 8 python train_framework.py --size 'normal' --gwm true --out 'result_normal' --pairlr 1e-2 --lr 1e-4 --decay_iter 20000 --batch_size 20 --epoch 1000 --gpu --frequency 20 --hdim 100 --nn_hidden_dim 50 --n_layers 3 --concat_hidden false --weight_tying false 

# use test.txt.proc for inference:

python inference.py --out 'result_all/inference1' --hdim 100 --n_layers 3 --nn_hidden_dim 50 --concat_hidden false --weight_tying false --gwm true --g_stop 'result_all/stop_snapshot_{iteration1}' --g_atom 'result_all/atom_snapshot_{iteration2}' --g_pair 'result_all/pair_snapshot_{iteration3}' --g_action 'result_all/action_snapshot_{iteration4}'

# calculate accuracy:

python acc.py --path 'result_all/inference1_final.txt'

```