deepcoy_combine.py

import dask.dataframe as dd
import pandas as pd
import sys
import os
import numpy as np

from Bio.PDB import PDBList
from Bio import SeqIO

import warnings

def get_sequence(pdb_id):
    try:
        pdbfile = PDBList().retrieve_pdb_file(pdb_id.upper(),file_format='pdb',pdir='/tmp')
        seq = str(next(SeqIO.parse(pdbfile, "pdb-seqres")).seq)
        os.unlink(pdbfile)

        return seq
    except Exception as e:
        print(e)
        pass

if __name__ == '__main__':
    import glob

    filenames = glob.glob(sys.argv[3])

    seqs = []
    smiles = []
    active = []

    targets = pd.read_csv(sys.argv[1],sep=' ',keep_default_na=False)
    for fn in filenames:
        df = pd.read_csv(fn,header=None,sep=' ')
        actives = df[0].unique()
        decoys = df[1].unique()
        smiles += actives.tolist()+decoys.tolist()
        active += [True]*len(actives) + [False]*len(decoys)
        split = os.path.basename(fn).split('-')
        target = split[2].upper()
        if len(split) > 5:
            target += '-'+split[3].upper()
        print(target)
        seq = get_sequence(targets[targets.name.str.upper()==target].pdb.values[0])
        seqs += [seq]*(len(actives)+len(decoys))

    ddf = dd.from_pandas(pd.DataFrame({'seq': seqs, 'smiles': smiles, 'active': active}),npartitions=1)
    ddf = ddf.repartition(partition_size='1M')
    ddf.to_parquet(sys.argv[2])