Skip to content

Latest commit

 

History

History
88 lines (70 loc) · 4.68 KB

README.md

File metadata and controls

88 lines (70 loc) · 4.68 KB
Raw

CoPriNet

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.

Installation

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

Scoring molecules

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

Retraining the model

Preparing dataset

If you are using your own dataset, and not the Mcule catalogue, go to step 2

  1. 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.

  1. Create the dataset from the chunked .csv files that follow the pattern in the variable pricePrediction.config.RAW_DATA_FILE_SUFFIX The 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
  1. 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/