YaSAScore

Source code for our paper "Prediction of compound synthesis accessibility based on reaction knowledge graph"

The code was built based on CMPNN (https://github.com/SY575/CMPNN), SYBA(https://github.com/lich-uct/syba). Thanks a lot for their code sharing!

Prediction results of compound synthesis accessibility (SA) based on the a refined chemical network constructed on the USPTO and Pistachio reaction datasets.

The workflow of YaSAScore

The Subgraph of the reaction knowledge graph network

The reaction paths of product (node: 2070779)

The performance of different models on the balanced test set

Partition Criterion	MODEL	AUC	ACC	MCC
SRP:2	DNN-PCD	0.576	0.511	0.044
SRP:2	RF-PCD	0.632	0.592	0.183
SRP:2	CMPNN	0.784	0.711	0.432
SRP:2	DNN-ECFP	0.72	0.667	0.333
SRP:2	SYBA	0.491	0.505	0.02
SRP:2	SYBA-2	0.739	0.668	0.343
SRP:2	SAScore	0.535	0.501	-0.03
SRP:2	SCScore	0.613	0.55	0.128
SRP:3	DNN-PCD	0.615	0.584	0.168
SRP:3	RF-PCD	0.627	0.588	0.177
SRP:3	CMPNN	0.791	0.715	0.434
SRP:3	DNN-ECFP	0.751	0.687	0.373
SRP:3	SYBA	0.465	0.496	-0.012
SRP:3	SYBA-2	0.76	0.69	0.382
SRP:3	SAScore	0.513	0.5	-0.011
SRP:3	SCScore	0.621	0.543	0.116
SRP:4	DNN-PCD	0.6	0.565	0.132
SRP:4	RF-PCD	0.627	0.583	0.168
SRP:4	CMPNN	0.814	0.733	0.466
SRP:4	DNN-ECFP	0.802	0.732	0.465
SRP:4	SYBA	0.448	0.491	-0.061
SRP:4	SYBA-2	0.8	0.727	0.453
SRP:4	SAScore	0.45	0.512	-0.021
SRP:4	SCScore	0.591	0.517	0.082

Quick start

Template extract

cd template_extract
conda env create -f template_extract.yaml  ### create env
conda activate casp_env_tf2   ### change env
mkdir uspto_pistachio_split_folder
mkdir uspto_pistachio_result
cd uspto_pistachio_split_folder
split -l 100 ../uspto_and_pistachio_top100.csv -d -a 3 uspto_pistachio_
cd ..
python Template_Extraction_and_Validation.py -d uspto_pistachio_split_folder -o uspto_pistachio_result -f template -r 1

Generate chemical_reaction_network and get minimum reaction steps

cd chemical_reaction_network_graph
mkdir uspto_reaction_split
cd uspto_reaction_split
split -l 1000 ../../data/chemical_reaction_network/all_reaction_uspto.csv -d -a 4
cd ..
python generate_reaction_structure_relationship.py -d uspto_reaction_split -o uspto_reaction_split_result -s ../data/chemical_reaction_network/all_structure_uspto.csv
cd uspto_reaction_split_result
nawk 'FNR==1 && NR!=1{next;}{print}' * > ../../data/chemical_reaction_network/reaction_to_structure_USPTO.csv #### combine the split relation file to reaction_to_structure_USPTO.csv
cd ../../
python generate_network_multiprocess.py -i ../data/chemical_reaction_network/reaction_to_structure_USPTO.csv -o ../data/chemical_reaction_network/uspto_graph_with_relationship.graphml -ro ../data/chemical_reaction_network/uspto_graph_with_relationship_reverse.graphml -d ../data/chemical_reaction_network/uspto_graph_degree.csv -n_cpu 10
python get_reaction_steps.py -gf ../../data/chemical_reaction_network/uspto_graph_reverse.graph -df ../../data/chemical_reaction_network/degree.csv -rf ../../data/chemical_reaction_network/reaction_to_structure_USPTO_test.csv -o ../../data/chemical_reaction_network/shortest_path

note: some big file such as reaction_all_structure_uspto, reaction_to_structure_USPTO.csv, uspto_graph_with_relationship.graphml are available at google drive

CMPNN training and predict

cd CMPNN-master
conda env create -f cmpnn.yaml ### Create env
conda activate cmpnn

Traing process by apply cmpnn model (without cross-validatte)

python train.py --data_path ../data/cmpnn_data/24w_cmpnn.csv 
                --dataset_type classification 
                --num_folds 1 
                --gpu 0 
                --seed 0 
                --epochs 30 
                --train_csv ../data/cmpnn_data/24w_train_df_seed0.csv 
                --validate_csv ../data/cmpnn_data/24w_val_df_seed0.csv 
                --test_csv ../data/cmpnn_data/24w_test_df_seed0.csv
python predict.py --data_path ../data/cmpnn_data/24w_test_df_seed0.csv --checkpoint_dir ckpt_for_3_split

SYBA-2 training

cd sascore_scscore_syba_syba2_model/scipt
conda env create -f syba_environment.yaml ### Create env
conda activate syba_env ### activate env
cd script
python syba-2_training.py --HS_train ../../data/syba_data/24w_train_HS.csv
                          --ES_train ../../data/syba_data/24w_train_ES.csv 
                          --count_file ../../data/syba_data/syba_ES_cluster_HS_train_val.csv 
                          --score_file ../../data/syba_data/syba_ES_cluster_HS_score_train_val.csv  #### Get count_file and score_file
cp ../../data/syba_data/syba_ES_cluster_HS_train_val.csv ../syba/resources ### Then put the score result "syba_ES_cluster_HS_score_train_val.csv" to syba/resources


Note: AS when training SYBA, separate (ES and HS) file are needed, that are 24w_train_HS.csv and 24w_train_ES.csv. 
The two files are recomined from 24w_train_df_seed0.csv and 24w_val_df_seed0.csv. The purpose of doing this is to provide the comparative result of different models

syba, syba-2, sascore, scscore predict

cd sascore_scscore_syba_syba2_model/scipt

### Get the scoring value of 24w_test_file
python diff_score_result.py --in_file ../../data/syba_data/24w_test_df_seed0.csv
                            --out_file ../../data/syba_data/24w_test_df_seed0_syba_and_mysyba.csv

#### Get the scoring value of all_remain_test_file (63w)
python diff_score_result.py --in_file ../../data/syba_data/24w_cmpnn_remain_all_test.csv
                            --out_file ../../data/syba_data/24w_test_df_seed0_syba_and_mysyba_all_test.csv

DNN training and predict

Note: If split by shortest reaction steps 3, train file is dnn_data/24w_train_df_seed0.csv, validate file is dnn_data/24w_val_df_seed0.csv, test file is dnn_data/24w_test_df_seed0.csv
      When adding the remained test items, the test file is dnn_data/24w_cmpnn_remain_all_test.csv

      If split by shortest reaction steps 2, train file is dnn_data/60w_train_df_seed0.csv, validate file is dnn_data/60w_val_df_seed0.csv, test file is dnn_data/60w_test_df_seed0.csv
      When adding the remained test items, the test file is dnn_data/60w_cmpnn_remain_all_test_2_split.csv

      If split by shortest reaction steps 4, train file is dnn_data/8w_train_df_seed0.csv, validate file is dnn_data/8w_val_df_seed0.csv, test file is dnn_data/8w_test_df_seed0.csv
      When adding the remained test items, the test file is dnn_data/8w_cmpnn_remain_all_test_4_split.csv

cd DNN_model
python train.py --train_file ../../data/dnn_data/24w_train_df_seed0.csv
                --val_file ../../data/dnn_data/24w_val_df_seed0.csv
                --save_path ../../data/dnn_data/split_by_3
                --project_name split_3
                --gpu_index 0

python predict.py --model_path ../../data/dnn_data/split_by_3/split_3.hdf5
                  --test_file  ../../data/dnn_data/24w_test_df_seed0.csv
                  --save_path  ../../data/dnn_data/split_by_3
                  --project_name split_3

View ES and HS distribution

cd picture
python get_ES_HS_file.py --train_file ../data/cmpnn_data/24w_train_df_seed0.csv
                         --val_file   ../data/cmpnn_data/24w_val_df_seed0.csv
                         --ES_out     ../data/cmpnn_data/24w_ES.csv
                         --HS_out     ../data/cmpnn_data/24w_HS.csv

python generate_physicochemical_property.py --ES_file ../data/cmpnn_data/24w_ES.csv
                                            --HS_file ../data/cmpnn_data/24w_HS.csv
                                            --out     ../data/cmpnn_data/24w_ES_HS_property.csv

python plot_physicochemical_property.py --in_file ../data/cmpnn_data/24w_ES_HS_property.csv
                                        --threshold 3
                                        --out 24w_property_kdeplot.png

python chemical_space_all_training_data.py --train_file ../data/cmpnn_data/24w_train_df_seed0.csv
                                           --val_file   ../data/cmpnn_data/24w_val_df_seed0.csv
                                           --test_file  ../data/cmpnn_data/24w_test_df_seed0.csv
                                           --pca_result  ../data/cmpnn_data/24w_pca_result.csv
                                           --threshold   3
                                           --out         24w_pca_picture.png

The distribution of the dataset

PCA analysis on the physicochemical descriptors of Subsets