CoPriNet is a Graph Neural Network trained on pairs of molecule 2D graphs and catalogue prices. CoPriNet predictions can be used as a proxy score for compound availability.
In order to install requirements, use conda to create an environment form the CoPriNet_env.yml file
conda env create -f CoPriNet_env.yml
conda activate CoPriNet
In order to execute CoPriNet you only need to prepare a csv file with your SMILES and execute the following command
python -m pricePrediction.predict path/to/csvFile -o path/toResults
E.g.
~/CoPriNet$ python -m pricePrediction.predict data/testData/npnp_dataset.csv -o npnp_scored.csv
For a complete description of the available options use:
python -m pricePrediction.predict -h
If you are using your own dataset, and not the Mcule catalogue, go to step 2
- Split the Mcule catalogue file into train, test, and val .csv files and chunk them into smaller files. The raw Mcule catalogue file should contain, at least, the following columns:"Mcule ID,SMILES,price 1 (USD),amount 1 (mg),delivery time 1 (w.days),available amount 1 (mg)".
python -m pricePrediction.selectFromRawData.selectFromRawDataNonVirtualAll --full_dataset_fname path/to/Mcule/dataset.csv --computed_datadir ./prepared_partions
e.g.
~/CoPriNet$ python -m pricePrediction.selectFromRawData.selectFromRawDataNonVirtualAll --full_dataset_fname mcule_purchasable_full_prices_210616_O9Jw1D_only_READILY.csv.gz --computed_datadir ./prepared_partions
~/CoPriNet$ ls prepared_partions/
mcule_full_test.csv_split_0.csv mcule_full_train.csv_split_10.csv mcule_full_train.csv_split_22.csv mcule_full_train.csv_split_34.csv
mcule_full_test.csv_split_1.csv mcule_full_train.csv_split_11.csv mcule_full_train.csv_split_23.csv mcule_full_train.csv_split_35.csv
mcule_full_train.csv_split_00.csv mcule_full_train.csv_split_12.csv mcule_full_train.csv_split_24.csv mcule_full_train.csv_split_36.csv
mcule_full_train.csv_split_01.csv mcule_full_train.csv_split_13.csv mcule_full_train.csv_split_25.csv mcule_full_train.csv_split_37.csv
mcule_full_train.csv_split_02.csv mcule_full_train.csv_split_14.csv mcule_full_train.csv_split_26.csv mcule_full_train.csv_split_38.csv
mcule_full_train.csv_split_03.csv mcule_full_train.csv_split_15.csv mcule_full_train.csv_split_27.csv mcule_full_train.csv_split_39.csv
mcule_full_train.csv_split_04.csv mcule_full_train.csv_split_16.csv mcule_full_train.csv_split_28.csv mcule_full_train.csv_split_40.csv
mcule_full_train.csv_split_05.csv mcule_full_train.csv_split_17.csv mcule_full_train.csv_split_29.csv mcule_full_val.csv_split_0.csv
mcule_full_train.csv_split_06.csv mcule_full_train.csv_split_18.csv mcule_full_train.csv_split_30.csv mcule_full_val.csv_split_1.csv
mcule_full_train.csv_split_07.csv mcule_full_train.csv_split_19.csv mcule_full_train.csv_split_31.csv params.txt
mcule_full_train.csv_split_08.csv mcule_full_train.csv_split_20.csv mcule_full_train.csv_split_32.csv
mcule_full_train.csv_split_09.csv mcule_full_train.csv_split_21.csv mcule_full_train.csv_split_33.csv
Chunked files contain two columns, "SMILES,price" and will be named following the pattern:
RAW_DATA_FILE_SUFFIX = r"mcule_full_(train|test|val)\.csv_split_\w+\.csv$"
The file pattern can be edited changing the pricePrediction/config.py file. If you are using your own catalogue
you may want to split it manually into tran/test/validation and chunk the partitions into smaller files. Each chunked
file should contain the header SMILES,price and the prices should be in $/g.
- Create the dataset from the chunked .csv files that follow the pattern in the variable
pricePrediction.config.RAW_DATA_FILE_SUFFIXThe files should be csv files with two columns named SMILES,price. price should be in $/g.
- Using the default paths included in
pricePrediction/config.py:python -m pricePrediction.preprocessData.prepareDataMol2Price - Manually specifying the raw data chunks directory and the directory to store the prepared datasets. Set -n N to use N cpus.
python -m pricePrediction.preprocessData.prepareDataMol2Price -i /path/to/dir/with/csvFiles -o /path/to/save/prepared/data #e.g. python -m pricePrediction.preprocessData.prepareDataMol2Price -i ./prepared_partions -o ./dataset_encoded/ -n 32
- Train the network
python -m pricePrediction.train.trainNet -m "one messege to describe the training" --encodedDir /path/to/save/prepared/data
e.g.
python -m pricePrediction.train.trainNet -m "trial01" --encodedDir ./dataset_encoded/