/GraphRXN

GraphRXN

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GraphRXN

Source code for our paper " GraphRXN: A Novel Representation for Reaction Prediction". The code was built based on CMPNN (https://github.com/SY575/CMPNN), DeepReac (https://github.com/bm2-lab/DeepReac), YieldBert (https://github.com/bm2-lab/DeepReac). Thanks a lot for their sharing.

Figure 1. Model architecture of GraphRXN

Figure1

Figure 2. General workflow of HTE process

Figure2

Figure 3. Reaction scheme and substrate scope

Figure3

Figure 4. Distribution of Ratio(UV), where A represents amine, and B represents bromide

Figure4

Figure 5. The scatter plots of GraphRXN on the entire dataset

Figure5

Model Performance of three public datasets over ten-fold CV on test set

Dataset Methods R2 MAE RMSE
Dataset1 GraphRXN-concat 0.951 4.30 5.98
Dataset1 GraphRXN-sum 0.937 4.85 6.80
Dataset1 Yield-BERT 0.951 4.00 6.03
Dataset1 DeepReac+ 0.922 5.25 7.54
Dataset2 GraphRXN-concat 0.844 7.94 11.08
Dataset2 GraphRXN-sum 0.838 8.09 11.29
Dataset2 Yield-BERT 0.815 8.13 12.08
Dataset2 DeepReac+ 0.827 8.06 11.65
Dataset3 GraphRXN-concat 0.892 0.16 0.23
Dataset3 GraphRXN-sum 0.881 0.18 0.24
Dataset3 Yield-BERT 0.886 0.16 0.24
Dataset3 DeepReac+ 0.853 0.18 0.25

Model performance of in-house dataset over 5-fold CV on test set

Groupe Size methods R2 MAE RMSE
Entire 1558 GraphRXN-concat 0.713 0.06 0.09
Entire 1558 GraphRXN-sum 0.704 0.06 0.09
Entire 1558 Yield-BERT 0.645 0.10 0.07
Entire 1558 DeepReac+ 0.610 0.07 0.10
G1 317 GraphRXN-concat 0.661 0.08 0.11
G1 317 GraphRXN-sum 0.462 0.11 0.14
G1 317 Yield-BERT 0.718 0.07 0.10
G1 317 DeepReac+ 0.551 0.09 0.13
G2 419 GraphRXN-concat 0.629 0.05 0.07
G2 419 GraphRXN-sum 0.592 0.06 0.07
G2 419 Yield-BERT 0.512 0.06 0.08
G2 419 DeepReac+ 0.528 0.06 0.08
G3 401 GraphRXN-concat 0.802 0.06 0.08
G3 401 GraphRXN-sum 0.775 0.06 0.08
G3 401 Yield-BERT 0.785 0.06 0.08
G3 401 DeepReac+ 0.745 0.07 0.09
G4 421 GraphRXN-concat 0.459 0.08 0.12
G4 421 GraphRXN-sum 0.419 0.09 0.12
G4 421 Yield-BERT 0.503 0.08 0.11
G4 421 DeepReac+ 0.23 0.10 0.14

Quick start

GraphRXN

conda env create -f GraphRXN.yaml ### Create GraphRXN env
conda activate GraphRXN
python reaction_train.py  --data_path data_scaler/Buchward-Hartwig/random_split/FullCV_01_train_temp_scaler.csv
                          --separate_test_path data_scaler/Buchward-Hartwig/random_split/FullCV_01_test_temp_scaler.csv
                          --dataset_type regression 
                          --num_folds 1 
                          --gpu 0 
                          --epochs 100 
                          --batch_size 128 
                          --save_dir ./result/Buchward/concat_01_temp
                          --metric r2 
                          --reaction_agg_method concat
Note: If choosing summation aggregation method, please specify --reaction_agg_method sum

DeepReac+

cd DeepReac
conda env create -f DeepReact.yaml ### Create DeepReac+ env
conda activate DeepReact
### train and predict
python DeepReac_train.py -train data_scaler/Buchward-Hartwig/random_split/FullCV_01_train_temp_scaler.csv
                         -test data_scaler/Buchward-Hartwig/random_split/FullCV_01_test_temp_scaler.csv
                         -epochs 100
                         -stats ./result_scaler/Buchward_01_test_stats.csv

Yield-BERT

cd Yield-BERT
conda env create -f rxnyields.yaml ### create Yield-BERT env
conda activate rxnyields

cd yield-BERT_baseline

### For Dataset 1 (Buchwald) training
python launch_buchwald_hartwig_training.py

### For Dataset 2 (Suzuki) training
python lauch_suzuki_miyaura_training.py

### For Dataset 3 (Denmark) training
python data3_training_10cv.py

### For in-house dataset training
python inhouse_data_transform.py