Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction

The directory contains source code of the article: Zhong et al's Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction.

In this work, we propose root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES, to narrow the string representation discrepancy for more efficient retrosynthesis. Here we provide the source code of our method.

Data and Model

USPTO-50K: https://github.com/Hanjun-Dai/GLN

USPTO-MIT: https://github.com/wengong-jin/nips17-rexgen/blob/master/USPTO/data.zip

USPTO-FULL: https://github.com/Hanjun-Dai/GLN

Our augmented datasets, checkpoints and 200 examples of attention maps: https://drive.google.com/drive/folders/1c15h6TNU6MSNXzqB6dQVMWOs2Aae8hs6?usp=sharing

Environment Preparation

Please make sure you have installed anaconda. The version about pytorch and cudatoolkit should be depended on your machine. The version of pytorch should not be smaller than 1.6 according to the OpenNMT-py.

conda create -n r-smiles python=3.7 \
conda activate r-smiles \
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 \
pip install pandas==1.3.4 \
pip install textdistance==4.2.2 \
conda install rdkit=2020.09.1.0 -c rdkit \
pip install OpenNMT-py==2.2.0

Overview of the workflow

We follow the OpenNMT architecture to train the Transformer. The workflow is

generate the dataset with root alignment and data augmentation;
build the vocab based on the dataset;
start training;
averaging the checkpoints to get the final checkpoint.
choose a checkpoint to make model predictions.
score the prediction to get the accuracy.

We have placed all the config files in the pretrain_finetune and train-from-scratch folders and categorized by task. You can use config files directly with OpenNMT commands or modify them according to your needs (such as different datasets or using our prepared checkpoints).

Data preprocessing

Step 1: You should download the dataset and put them like this:

USPTO-50K: dataset/USPTO_50K/raw_train.csv
USPTO-MIT: dataset/USPTO-MIT/train.txt
USPTO_full: dataset/USPTO_full/raw_train.csv

Step 2: Generate the pretrain data with data augmentation (you can skip this step if you want to train from scratch)

python preprocessing/generate_pretrain_data.py.py \
	-mode <reactant only or product only or both of them> \
    -augmentation <times of augmentation>
python preprocessing/generate_masked_pretrain_data.py \
	-dataset <the dataset your want>

Here is the example for using the product molecules in USPTO_full:

python preprocessing/generate_pretrain_data.py -mode product -augmentation 5
python preprocessing/generate_masked_pretrain_data.py -dataset USPTO_full_pretrain_aug5_product

Step 3: Generate the dataset with root alignment and data augmentation

python preprocessing/generate_PtoR_data.py \
	-dataset <the dataset you want> \
    -augmentation <times of augmentation> \
    -processes <number of process you want to use. Higher is faster.>

Here is the example for different datasets:

python preprocessing/generate_PtoR_data.py -dataset USPTO_50K -augmentation 20 -processes 8
python preprocessing/generate_PtoR_data.py -dataset USPTO-MIT -augmentation 5 -processes 8
python preprocessing/generate_PtoR_data.py -dataset USPTO_full -augmentation 5 -processes 8

It is the same procedure for generate_RtoP_data and generate_PtoStoR_data.

python preprocessing/generate_RtoP_data.py -dataset USPTO-MIT -augmentation 5 -processes 8 -separated
python preprocessing/generate_RtoP_data.py -dataset USPTO-MIT -augmentation 5 -processes 8 -postfix _mixed
python preprocessing/generate_PtoStoR_data.py -dataset USPTO_50K -augmentation 20 -processes 8

Pretrain and Finetune

(If you want to pretrain the model, you should also generate all the finetune datasets in advance to build a full vocab.)

Step 1 (Pretrain): Just run the prepared shell command to start pretraining according to your pretrain dataset.
```
bash shell_cmds/product_pretrain.sh
bash shell_cmds/reactant_pretrain.sh
bash shell_cmds/both_pretrain.sh
```
For example, if you want to pretrain the datasets that contains product molecules only, you should run the first command.

Step 2 (Finetune): Run the OpenNMT train command with the prepared parameter config file. If you want to use different checkpoint to finetune the model, you should modify train_from parameter in the corresponding config file.

onmt_train -config pretrain_finetune/finetune/PtoR/PtoR-50K-aug20-config.yml
onmt_train -config pretrain_finetune/finetune/PtoR/PtoR-MIT-aug5-config.yml
onmt_train -config pretrain_finetune/finetune/PtoR/PtoR-Full-aug5-config.yml
onmt_train -config pretrain_finetune/finetune/RtoP/RtoP-MIT-mixed-aug5-config.yml
onmt_train -config pretrain_finetune/finetune/RtoP/RtoP-MIT-separated-aug5-config.yml

Step 3 (Average): Run the prepared shell command to average the checkpoints and get the final checkpoint. You can modify the corresponding shell command to average different checkpoints.

bash pretrain_finetune/finetune/PtoR/PtoR-50K-aug20-average.sh
bash pretrain_finetune/finetune/PtoR-MIT-aug5-average_models.sh
bash pretrain_finetune/finetune/PtoR-Full-aug5-average_models.sh
bash pretrain_finetune/finetune/RtoP/RtoP-MIT-mixed-aug5-average_models.sh
bash pretrain_finetune/finetune/RtoP/RtoP-MIT-separated-aug5-average_models.sh

Train from scratch

Step 1: Just run the prepared shell command to start training according to your task.

bash shell_cmds/p2r_50k_from_scratch.sh
bash shell_cmds/p2r_MIT_from_scratch.sh
bash shell_cmds/p2r_full_from_scratch.sh
bash shell_cmds/r2p_MIT_mixed_from_scratch.sh
bash shell_cmds/r2p_MIT_separated_from_scratch.sh
bash shell_cmds/p2s_50k_from_scratch.sh
bash shell_cmds/s2r_50k_from_scratch.sh

Step 2: Run the prepared shell command to average the checkpoints and get the final checkpoint. You can modify the corresponding shell command to average different checkpoints.

bash train-from-scratch/PtoR/PtoR-50K-aug20-average.sh
bash train-from-scratch/PtoR/PtoR-MIT-aug5-average_models.sh
bash train-from-scratch/PtoR/PtoR-Full-aug5-average_models.sh
bash train-from-scratch/RtoP/RtoP-MIT-mixed-aug5-average_models.sh
bash train-from-scratch/RtoP/RtoP-MIT-separated-aug5-average_models.sh
bash train-from-scratch/PtoS/PtoS-50K-aug20-average.sh
bash train-from-scratch/StoR/StoR-50K-aug20-average.sh

Translate and score

P2R / R2P / P2S / S2R

Step 1: Run the OpenNMT translate command with the prepared parameter config file. If you want to use different checkpoint, you should modify model parameter in the corresponding config file.

For example, if you want to make retrosynthesis predictions with a train-from-scratch checkpoint for USPTO-50K, you should run the following commands:
```
onmt_translate -config train-from-scratch/PtoR/PtoR-50K-aug20-translate.yml
```

Step 2: Score the predictions.

python score.py \
	-beam_size	<the beam_size when translating, default is 10> \
	-n_best	<the n_best when translating, default is 10> \
	-augmentation <times of augmentation when making predictions> \
	-predictions <the path of the prediction result> \
	-targets <the path of the augmented target> \
	-process_number <number of process you want to use. Higher is faster.> \
	-score_alpha <weighting the number of occurrences and ranking, default is 1> \
	-save_file <save the final prediction resutls>
	-detailed <if you want to see the detailed accuracy like chirality, set it True> \
	-source <is only needed if the detailed is true. the path of augmented source> \
	-synhton <if you want to calculate the accuracy of synthons, set it True>

Here is an example for scoring the USPTO-50K when making prediction with 20 times augmentation:

python score.py \
	-beam_size 10 \
	-n_best 10 \
	-augmenation 20 \
	-targets ./dataset/USPTO_50K_PtoR_aug20/test/tgt-test.txt \
	-predictions ./exp/USPTO_50K_PtoR_aug20/average_model_56-60-results.txt \
	-process_number 8\
	-score_alpha 1 \
	-save_file ./final_results.txt
	-detailed
	-source ./dataset/USPTO_50K_PtoR_aug20/test/src-test.txt

P2S2R

Step 1: Get the predictions of P2S stage:

onmt_translate -config train-from-scratch/PtoS/PtoS-50K-aug20-translate.yml

Step 2: Select top-k synthons to preform data augmentation for S2R stage:

python preprocessing/prepare_StoR.py \
	-ptos_src ./dataset/USPTO_50K_P2S2R_aug20/P2S/test/src-test.txt
	-ptos_results exp/USPTO_50K_P2S2R_aug20/P2S/average_model_96-100-results.txt \
	-ptos_beam_size 10 \
	-ptos_topk 3 \
	-stor_targets ./dataset/USPTO_50K_P2S2R_aug20/S2R/test/tgt-test.txt \
	-augmentation 20 \
	-save_dir ./dataset/USPTO_50K_P2S2R_aug20/S2R/test/

Step 3: Get the predictions of P2S2R:

onmt_translate -config train-from-scratch/StoR/PtoStoR-50K-aug20-translate.yml

Step 4: Score the predictions.

python score_p2s2r.py \
	-beam_size	<the beam_size when translating, default is 10> \
	-n_best	<the n_best when translating, default is 10> \
	-augmentation <times of augmentation when making S2R predictions> \
	-predictions <the path of the prediction result> \
	-ptos_topk <should be same as the `prepare_StoR`>
	-targets <the path of the augmented target> \
	-process_number <number of process you want to use. Higher is faster.> \
	-save_file <save the final prediction resutls>

Generate your own R-SMILES

Simply prepare your atom mapped reaction and run the following script. If your reaction is without atom mapping, we recommend you to use Indigo or RXNmapper tools to get it.

python preprocessing/get_R-SMILES.py \
	-rxn <atom mapped rxn>
	-mode <'retro' or 'forward'>
	-forewad_mode <'separated' or 'mixed'>
	-augmenation <times of augmentation>

Here is an example to run the script and results:

python preprocessing/get_R-SMILES.py -rxn '[O:1]=[N+:2]([O-:3])[c:4]1[cH:5][c:6]([CH2:7][OH:8])[cH:9][cH:10][c:11]1[F:12]>>[O:1]=[N+:2]([O-:3])[c:4]1[cH:5][c:6]([CH:7]=[O:8])[cH:9][cH:10][c:11]1[F:12]' \
-augmentation 10

Namespace(augmentation=10, forward_mode='separated', mode='retro', processes=-1, rxn='[O:1]=[N+:2]([O-:3])[c:4]1[cH:5][c:6]([CH2:7][OH:8])[cH:9][cH:10][c:11]1[F:12]>>[O:1]=[N+:2]([O-:3])[c:4]1[cH:5][c:6]([CH:7]=[O:8])[cH:9][cH:10][c:11]1[F:12]', seed=33)
Original input: [O:1]=[N+:2]([O-:3])[c:4]1[cH:5][c:6]([CH:7]=[O:8])[cH:9][cH:10][c:11]1[F:12]
Original output: [O:1]=[N+:2]([O-:3])[c:4]1[cH:5][c:6]([CH2:7][OH:8])[cH:9][cH:10][c:11]1[F:12]
Canonical input: O=Cc1ccc(F)c([N+](=O)[O-])c1
Canonical output: O=[N+]([O-])c1cc(CO)ccc1F
ID:0
R-SMILES input:O=Cc1ccc(F)c([N+](=O)[O-])c1
R-SMILES output:OCc1ccc(F)c([N+](=O)[O-])c1
ID:1
R-SMILES input:O=Cc1ccc(F)c([N+](=O)[O-])c1
R-SMILES output:OCc1ccc(F)c([N+](=O)[O-])c1
ID:2
R-SMILES input:c1([N+](=O)[O-])cc(C=O)ccc1F
R-SMILES output:c1([N+](=O)[O-])cc(CO)ccc1F
ID:3
R-SMILES input:C(=O)c1ccc(F)c([N+](=O)[O-])c1
R-SMILES output:C(O)c1ccc(F)c([N+](=O)[O-])c1
ID:4
R-SMILES input:c1cc(F)c([N+](=O)[O-])cc1C=O
R-SMILES output:c1cc(F)c([N+](=O)[O-])cc1CO
ID:5
R-SMILES input:c1c(C=O)ccc(F)c1[N+](=O)[O-]
R-SMILES output:c1c(CO)ccc(F)c1[N+](=O)[O-]
ID:6
R-SMILES input:c1(C=O)ccc(F)c([N+](=O)[O-])c1
R-SMILES output:c1(CO)ccc(F)c([N+](=O)[O-])c1
ID:7
R-SMILES input:c1(F)ccc(C=O)cc1[N+](=O)[O-]
R-SMILES output:c1(F)ccc(CO)cc1[N+](=O)[O-]
ID:8
R-SMILES input:[N+](=O)([O-])c1cc(C=O)ccc1F
R-SMILES output:[N+](=O)([O-])c1cc(CO)ccc1F
ID:9
R-SMILES input:[O-][N+](=O)c1cc(C=O)ccc1F
R-SMILES output:[O-][N+](=O)c1cc(CO)ccc1F
Avg. edit distance: 1.0

Translate with our prepared checkpoint

After downloading our checkpoint files, you should start by creating a translation config file with the following content:

model: <the path of trained model>
src: <the path of input>
output: <the path of output>
gpu: 0
beam_size: 10
n_best: 10
batch_size: 8192
batch_type: tokens
max_length: 1000
seed: 0

Then you can run the OpenNMT command to get the predictions:

onmt_translate -config <the path of config file>

Results

Forward Prediction

Top-50 exact match accuracy on the USPTO-MIT.

Reagents Separated:

Model	Top-1	Top-2	Top-5	Top-10	Top-20
Molecular Transformer	90.5	93.7	95.3	96.0	96.5
MEGAN	89.3	92.7	95.6	96.7	97.5
Augmented Transformer	91.9	95.4	97.0	-	-
Chemformer	92.8	-	94.9	95.0	-
Ours	92.3	95.9	97.5	98.1	98.5

Reagents Mixed:

Model	Top-1	Top-2	Top-5	Top-10	Top-20
Molecular Transformer	88.7	92.1	94.2	94.9	95.4
MEGAN	86.3	90.3	94.0	95.4	96.6
Augmented Transformer	90.4	94.6	96.5	-	-
Chemformer	91.3	-	93.7	94.0	-
Ours	91.0	95.0	96.8	97.0	97.3

Retrosynthesis

Top-50 exact match accuracy on the USPTO-50K.

Model	Top-1	Top-3	Top-5	Top-10	Top-20	Top-50
GraphRetro	53.7	68.3	72.2	75.5	-	-
RetroPrime	51.4	70.8	74.0	76.1	-	-
AT	53.5	-	81.0	85.7	-	-
LocalRetro	53.4	77.5	85.9	92.4	-	97.7
Ours(P2S2R)	49.1	68.4	75.8	82.2	85.1	88.7
Ours(P2R)	56.3	79.2	86.2	91.0	93.1	94.6

Top-50 exact match accuracy on the USPTO-MIT.

Model	Top-1	Top-3	Top-5	Top-10	Top-20	Top-50
LocalRetro	54.1	73.7	79.4	84.4	-	90.4
AutopSynRoute	54.1	71.8	76.9	81.8	-	-
RetroTRAE	58.3	-	-	-	-	-
Ours(P2R)	60.3	78.2	83.2	87.3	89.7	91.6

Top-50 exact match accuracy on the USPTO-FULL.

Model	Top-1	Top-3	Top-5	Top-10	Top-20	Top-50
RetroPrime	44.1	-	-	68.5	-	-
AT	46.2	-	-	73.3	-	-
LocalRetro	39.1	53.3	58.4	63.7	67.5	70.7
Ours(P2R)	48.9	66.6	72.0	76.4	80.4	83.1

Top-10 accuracy of product-to-synthon on the USPTO-50K.

Model	Top-1	Top-3	Top-5	Top-10
G2Gs	75.8	83.9	85.3	85.6
GraphRetro	70.8	92.2	93.7	94.5
RetroPrime	65.6	87.7	92.0	-
Ours	75.2	94.4	97.9	99.1

Top-10 accuracy of synthon-to-reactant on the USPTO-50K.

Model	Top-1	Top-3	Top-5	Top-10
G2Gs	61.1	81.5	96.7	90.0
GraphRetro	75.6	87.7	92.9	96.3
RetroPrime	73.4	87.9	89.8	90.4
Ours	73.9	91.9	95.2	97.4

Acknowledgement

OpenNMT-py: https://github.com/OpenNMT/OpenNMT-py

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
imgs		imgs
preprocessing		preprocessing
pretrain_finetune		pretrain_finetune
shell_cmds		shell_cmds
train-from-scratch		train-from-scratch
.gitignore		.gitignore
LICENSE		LICENSE
Readme.md		Readme.md
pyproject.toml		pyproject.toml
score.py		score.py
score_p2s2r.py		score_p2s2r.py

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kmaziarz/RootAligned

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