step-wise-chemical-synthesis-prediction

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step-wise-chemical-synthesis-prediction

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

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

## Proposal

![](https://raw.githubusercontent.com/pfnet-research/step-wise-chemical-synthesis-prediction/master/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:

![](https://raw.githubusercontent.com/pfnet-research/step-wise-chemical-synthesis-prediction/master/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.

![](https://raw.githubusercontent.com/pfnet-research/step-wise-chemical-synthesis-prediction/master/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'
```

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