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basic.py
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basic.py
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#! /usr/bin/python
# -*- coding: utf-8 -*-
## @Author: marcuschen
## @File: basic.py
## @Created Time: Mon Apr 5 11:36:14 2021
## @Description:
import os
import pdb
import torch
import logging
import numpy as np
import pandas as pd
import openbabel as ob
import pybel
from rdkit import Chem
from rdkit.Chem import AllChem
from pybel import Outputfile
from argparse import ArgumentParser
import multiprocessing
from multiprocessing import Pool
import sys
import os.path as osp
root = osp.dirname(osp.abspath(__file__))
print(root)
sys.path.insert(0, root)
from utils import calc_dist_min, load_smiles_list, OpenBabelCalculator, RDKitCalculator, calc_dist
logging.basicConfig(format="%(asctime)s, %(message)s", level=logging.INFO)
conversion = ob.OBConversion()
conversion.SetInAndOutFormats("smi", "mdl")
smi_conversion = ob.OBConversion()
smi_conversion.SetInAndOutFormats("smi", "can")
def parse_args():
parser = ArgumentParser(description="Basic statistical analysis for quantum chemistry dataset.")
parser.add_argument("--dataset", type=str, default="qm9", choices=["qm9", "pcqm4m"], help="dataset name (default: qm9)")
parser.add_argument("--subset_ratio", type=float, default=None, help="subset ratio (default: None for full dataset)")
parser.add_argument("--start_idx", type=int, default=0, help="start index for dataset to be analyzed (default: 0)")
parser.add_argument("--forcefield", type=str, default="mmff94", help="algorithm used for generating spatial information (default: mmff94)")
parser.add_argument("--op", type=str, default="statis", choices=["statis", "check", "contrast", "add"], help="op (default: statis)")
parser.add_argument("--max_size", type=int, default=-1, help="maximum size of dataset (default: -1 for all)")
parser.add_argument("--threshold", type=float, default=0.9, help="threshold for minimum interatomic distance in a molecule (default: 0.9)")
parser.add_argument("--max_steps", type=int, default=10000, help="maximum number of steps to calculate atomic coordinates (default: 10000)")
parser.add_argument("--retry_times", type=int, default=3, help="maximum number of retries (default: 3)")
parser.add_argument("--mp", action="store_true", help="whether to use multiprocessing (default: False)")
parser.add_argument("--nprocs", type=int, default=8, help="number of procesors (default: 8)")
parser.add_argument("--removehs", action="store_true", help="whether to remove hydrogen atoms (default: False)")
args = parser.parse_args()
return args
# helper functions
def parse_block(block, N):
pos = block.split("\n")[4: 4 + N]
pos = [[float(x) for x in line.split()[:3]] for line in pos]
pos = torch.tensor(pos, dtype=torch.float)
return pos
def optimize(smiles):
ob_mol = ob.OBMol()
conversion.ReadString(ob_mol, smiles)
steps = 50
w_bond_list = []
wo_bond_list = []
py_mol_list = []
pos_list = []
while steps < args.max_steps:
ob_calc = OpenBabelCalculator(ob_mol, forcefield=args.forcefield, steps=steps)
curr_min_dist_w_bond = ob_calc.get_min_dist(with_bond=True)
curr_min_dist_wo_bond = ob_calc.get_min_dist(with_bond=False)
py_mol = ob_calc.get_pymol()
pos = ob_calc.get_pos()
w_bond_list.append(curr_min_dist_w_bond)
wo_bond_list.append(curr_min_dist_wo_bond)
py_mol_list.append(py_mol)
pos_list.append(pos)
steps *= 2
max_idx = wo_bond_list.index(max(wo_bond_list))
max_py_mol = py_mol_list[max_idx]
max_pos = pos_list[max_idx]
return max_py_mol, max_pos, w_bond_list[max_idx], wo_bond_list[max_idx]
def proc_one_smiles(smiles):
ob_mol = ob.OBMol()
conversion.ReadString(ob_mol, smiles)
ob_calc = OpenBabelCalculator(ob_mol, forcefield=args.forcefield, removehs=args.removehs)
py_mol = ob_calc.get_pymol()
pos = ob_calc.get_pos()
curr_min_dist_w_bond = ob_calc.get_min_dist(with_bond=True)
curr_min_dist_wo_bond = ob_calc.get_min_dist(with_bond=False)
if curr_min_dist_w_bond is None or curr_min_dist_wo_bond is None:
pos = pos.numpy()
return pos, None, None, smiles
if curr_min_dist_wo_bond < args.threshold:
logging.info("Invalid: Minimum interatomic distance without bonds: {:.4f}, Smiles: {} ...".format(curr_min_dist_wo_bond, smiles))
retry = 0
curr_min_dist_wo_bond_ = curr_min_dist_wo_bond
while curr_min_dist_wo_bond_ < args.threshold and retry <= args.retry_times:
py_mol_, pos_, curr_min_dist_w_bond_, curr_min_dist_wo_bond_ = optimize(smiles)
retry += 1
py_mol = py_mol_
pos = pos_
curr_min_dist_w_bond = curr_min_dist_w_bond_
curr_min_dist_wo_bond = curr_min_dist_wo_bond_
logging.info(" Valid: Minimum interatomic distance without bonds: {:.4f}, Smiles: {} ...".format(curr_min_dist_wo_bond, smiles))
pos = pos.numpy()
return pos, curr_min_dist_w_bond, curr_min_dist_wo_bond, smiles
def statis_qm9_mp(filepath):
pool = multiprocessing.Pool(args.nprocs)
logging.info("Generating smiles ...")
supplier = Chem.SDMolSupplier(filepath, removeHs=False, sanitize=False)
smiles_list = [Chem.MolToSmiles(mol) for mol in supplier]
logging.info("Generate done ...")
fail_count = 0
smiles2idx = {smiles: idx for idx, smiles in enumerate(smiles_list)}
smiles2pos = {}
wo_bond = 100
w_bond = 100
for pos, curr_w_bond, curr_wo_bond, smiles in pool.imap(proc_one_smiles, smiles_list):
if smiles2idx[smiles] % 1000 == 0:
logging.info("Processing idx: {}, smiles: {}, fail count so far: {} ...".format(
smiles2idx[smiles], smiles, fail_count))
if curr_wo_bond < args.threshold:
fail_count += 1
assert isinstance(pos, np.ndarray)
smiles2pos[smiles] = pos
if curr_wo_bond < wo_bond:
wo_bond = curr_wo_bond
if curr_w_bond < w_bond:
w_bond = curr_w_bond
if smiles2idx[smiles] % 1000 == 0:
logging.info("minimum interatomic distance with bonds so far: {:.4f} ...".format(w_bond))
logging.info("minimum interatomic distance without bonds so far: {:.4f} ...".format(wo_bond))
new_smiles2pos = {}
for smiles, pos in smiles2pos.items():
new_smiles2pos[smiles] = torch.from_numpy(pos)
smiles2pos = new_smiles2pos
del new_smiles2pos
out_filepath = "./qm9_pos.pt"
torch.save(smiles2pos, out_filepath)
def statis_qm9(filepath):
supplier = Chem.SDMolSupplier(filepath, removeHs=False, sanitize=False)
w_bond = 100
wo_bond = 100
smiles2pos = {}
for i, mol in enumerate(supplier):
if i < args.start_idx:
continue
try:
smiles = Chem.MolToSmiles(mol)
except:
logging.info("Processing mol {}, parse smiles fail ...".format(i))
smiles = None
if i % 1000 == 0:
logging.info("Processing mol {}, smiles: {} ...".format(i, smiles))
# ob_mol: OpenBabel's molecule object
ob_mol = ob.OBMol()
conversion.ReadString(ob_mol, smiles)
ob_calc = OpenBabelCalculator(ob_mol, forcefield=args.forcefield, removehs=args.removehs)
py_mol = ob_calc.get_pymol()
pos = ob_calc.get_pos()
# sanitize
if mol is None:
logging.info("idx: {}, smiles: {} cannot be parsed ...".format(i, can_smiles))
continue
N = mol.GetNumAtoms()
try:
assert N == pos.size(0)
except:
pdb.set_trace()
tmp = 1
curr_min_dist_w_bond = ob_calc.get_min_dist(with_bond=True)
curr_min_dist_wo_bond = ob_calc.get_min_dist(with_bond=False)
if curr_min_dist_wo_bond < args.threshold:
logging.info("Invalid: Minimum interatomic distance without bonds: {:.4f}, Smiles: {} ...".format(curr_min_dist_wo_bond, smiles))
retry = 0
curr_min_dist_wo_bond_ = curr_min_dist_wo_bond
while curr_min_dist_wo_bond_ < args.threshold and retry <= args.retry_times:
py_mol_, pos_, curr_min_dist_w_bond_, curr_min_dist_wo_bond_ = optimize(smiles)
retry += 1
py_mol = py_mol_
pos = pos_
curr_min_dist_w_bond = curr_min_dist_w_bond_
curr_min_dist_wo_bond = curr_min_dist_wo_bond_
logging.info(" Valid: Minimum interatomic distance without bonds: {:.4f}, Smiles: {} ...".format(curr_min_dist_wo_bond, smiles))
min_dist = calc_dist(py_mol, pos, with_bond=False)
try:
assert min_dist == curr_min_dist_wo_bond
except:
logging.info("Fail smiles: {} ...".format(smiles))
smiles2pos[smiles] = pos
if curr_min_dist_w_bond < w_bond:
w_bond = curr_min_dist_w_bond
if curr_min_dist_wo_bond < wo_bond:
wo_bond = curr_min_dist_wo_bond
if i % 1000 == 0:
logging.info("minimum interatomic distance with bonds so far: {:.4f} ...".format(w_bond))
logging.info("minimum interatomic distance without bonds so far: {:.4f} ...".format(wo_bond))
if args.max_size >= 0 and i >= args.max_size:
break
out_filepath = "./qm9_pos.pt"
torch.save(smiles2pos, out_filepath)
def proc_pcqm4m_mp(smiles_list):
w_bond = 100
wo_bond = 100
smiles2pos = {}
fail_count = 0
fail_smiles = []
smiles2idx = {smiles: idx for idx, smiles in enumerate(smiles_list)}
pool = Pool(args.nprocs)
for pos, curr_w_bond, curr_wo_bond, smiles in pool.imap(proc_one_smiles, smiles_list):
if smiles2idx[smiles] % 1000 == 0:
logging.info("Processing idx: {}, smiles: {}, fail count so far: {} ...".format(
smiles2idx[smiles], smiles, fail_count))
if curr_w_bond is None or curr_wo_bond is None:
fail_count += 1
fail_smiles.append(smiles)
assert isinstance(pos, np.ndarray)
smiles2pos[smiles] = pos
if smiles2idx[smiles] % 1000 == 0:
logging.info("Minimum interatomic distance with bonds so far: {:.4f} ...".format(w_bond))
logging.info("Minimum interatomic distance without bonds so far: {:.4f} ...".format(wo_bond))
continue
if curr_wo_bond < args.threshold:
fail_count += 1
fail_smiles.append(smiles)
assert isinstance(pos, np.ndarray)
smiles2pos[smiles] = pos
if curr_wo_bond < wo_bond:
wo_bond = curr_wo_bond
if curr_w_bond < w_bond:
w_bond = curr_w_bond
if smiles2idx[smiles] % 1000 == 0:
logging.info("Minimum interatomic distance with bonds so far: {:.4f} ...".format(w_bond))
logging.info("Minimum interatomic distance without bonds so far: {:.4f} ...".format(wo_bond))
new_smiles2pos = {}
for smiles, pos in smiles2pos.items():
new_smiles2pos[smiles] = torch.from_numpy(pos)
smiles2pos = new_smiles2pos
del new_smiles2pos
return smiles2pos
def proc_pcqm4m_sp(smiles_list):
w_bond = 100
wo_bond = 100
smiles2pos = {}
fail_count = 0
fail_smiles = []
for i, smiles in enumerate(smiles_list):
if i % 1000 == 0:
logging.info("Processing idx: {}, smiles: {}, fail count so far: {} ...".format(i, smiles, fail_count))
# ob_mol: OpenBabel's molecule object
ob_mol = ob.OBMol()
conversion.ReadString(ob_mol, smiles)
ob_calc = OpenBabelCalculator(ob_mol, forcefield=args.forcefield, removehs=args.removehs)
curr_min_dist_w_bond = ob_calc.get_min_dist(with_bond=True)
curr_min_dist_wo_bond = ob_calc.get_min_dist(with_bond=False)
py_mol = ob_calc.get_pymol()
pos = ob_calc.get_pos()
pdb.set_trace()
if curr_min_dist_w_bond is None or curr_min_dist_wo_bond is None:
fail_count += 1
fail_smiles.append(smiles)
smiles2pos[smiles] = pos
if i % 1000 == 0:
logging.info("minimum interatomic distance with bonds so far: {:.4f} ...".format(w_bond))
logging.info("minimum interatomic distance without bonds so far: {:.4f} ...".format(wo_bond))
continue
if curr_min_dist_wo_bond < args.threshold:
logging.info("Invalid: Minimum interatomic distance without bonds: {:.4f}, Smiles: {} ...".format(curr_min_dist_wo_bond, smiles))
retry = 0
curr_min_dist_wo_bond_ = curr_min_dist_wo_bond
while curr_min_dist_wo_bond_ < args.threshold and retry <= args.retry_times:
py_mol_, pos_, curr_min_dist_w_bond_, curr_min_dist_wo_bond_ = optimize(smiles)
retry += 1
py_mol = py_mol_
pos = pos_
curr_min_dist_w_bond = curr_min_dist_w_bond_
curr_min_dist_wo_bond = curr_min_dist_wo_bond_
logging.info(" Valid: Minimum interatomic distance with bonds: {:.4f}, Smiles: {} ...".format(curr_min_dist_wo_bond, smiles))
min_dist = calc_dist(py_mol, pos, with_bond=False)
try:
assert min_dist == curr_min_dist_wo_bond
except:
logging.info("Fail smiles: {} ...".format(smiles))
fail_count += 1
fail_smiles.append(smiles)
smiles2pos[smiles] = pos
if curr_min_dist_w_bond < w_bond:
w_bond = curr_min_dist_w_bond
if curr_min_dist_wo_bond < wo_bond:
wo_bond = curr_min_dist_wo_bond
if i % 1000 == 0:
logging.info("minimum interatomic distance with bonds so far: {:.4f} ...".format(w_bond))
logging.info("minimum interatomic distance without bonds so far: {:.4f} ...".format(wo_bond))
if args.max_size >= 0 and i >= args.max_size:
break
return smiles2pos
def statis_pcqm4m(filepath, subset_ratio=None, start_idx=0):
logging.info("Loading smiles list ...")
smiles_list = load_smiles_list(os.path.join("../dataset", "pcqm4m_kddcup2021", "raw", "data.csv"))
smiles_list = smiles_list[start_idx:]
if subset_ratio is not None:
smiles_list = smiles_list[start_idx: start_idx + int(subset_ratio * len(smiles_list))]
logging.info("Number of smiles: {} ...".format(len(smiles_list)))
smiles2pos = proc_pcqm4m_sp(smiles_list)
out_filepath = "./pcqm4m_pos_sp.pt"
torch.save(smiles2pos, out_filepath)
logging.info("Fail smiles are listed as follows: ")
for idx, smiles in enumerate(fail_smiles):
print(idx, smiles)
def statis_pcqm4m_mp(filepath, subset_ratio=None, start_idx=0):
pool = multiprocessing.Pool(args.nprocs)
logging.info("Loading smiles list ...")
# 路径需要修改
smiles_list = load_smiles_list(os.path.join("../../dataset", "pcqm4m_kddcup2021", "raw", "data.csv"))
smiles_list = smiles_list[start_idx:]
if subset_ratio is not None:
smiles_list = smiles_list[start_idx: start_idx + int(subset_ratio * len(smiles_list))]
if args.max_size >= 0:
smiles_list = smiles_list[start_idx: start_idx + args.max_size]
logging.info("Number of smiles: {} ...".format(len(smiles_list)))
smiles2pos = proc_pcqm4m_mp(smiles_list)
out_filepath = "./pcqm4m_pos_mp.pt"
torch.save(smiles2pos, out_filepath)
logging.info("Fail smiles are listed as follows: ")
for idx, smiles in enumerate(fail_smiles):
print(idx, smiles)
def supplement_pcqm4m():
logging.info("Loading smiles2pos ...")
filepath = "./pcqm4m_pos_mp.pt"
smiles2pos = torch.load(filepath)
logging.info("Loading smiles list ...")
smiles_list = load_smiles_list(os.path.join("../dataset", "pcqm4m_kddcup2021", "raw", "data.csv"))
smiles_list_with_pos = list(smiles2pos.keys())
if len(smiles2pos) < len(smiles_list):
smiles_list_wo_pos = list(set(smiles_list) - set(smiles_list_with_pos))
pdb.set_trace()
if args.mp:
smiles2pos_wo_pos = proc_pcqm4m_mp(smiles_list_wo_pos)
else:
smiles2pos_wo_pos = proc_pcqm4m_sp(smiles_list_wo_pos)
smiles2pos.update(smiles2pos_wo_pos)
assert len(smiles2pos) == len(smiles_list)
def check_dist_min():
logging.info("Loading ...")
filepath = "../dataset/pcqm4m_kddcup2021/sdf/pybel_pos_mmff94.pt"
smiles2pos = torch.load(filepath)
logging.info("Loaded ...")
checked_smiles = [
"O=C1C=NC2C(=N1)C(=C(C=C2)F)C",
"NC1=c2c(N=N1)cc(=C)n(c2=O)C1CC1",
"NC1=c2c(N=N1)cc(=C)n(c2=O)C(C)C",
"CC1=CCCC(=C(CC1)C)C",
"COC(=O)C1CC(=C2C1C(C)CCC=C2)C",
"O=C1C(=C2C=CCC=C2C=Cc2c1cccc2)C",
"CNC1=CC2=NC(=CC(=O)C2C=C1)NC",
"O=C1N=C2N(C1)C(=O)C1C(=N2)C(=CS1)C",
"OCC1=c2cc(Cl)ccc2=NC(=O)C1=C",
"COC(=O)C1=C(NCCC(=CC1)C)C(=O)OC",
]
best_dist_min = 100
best_smiles = None
for idx, smiles in enumerate(checked_smiles):
pos = smiles2pos[smiles]
if idx % 1000 == 0:
logging.info("Processing idx: {}, smiles: {} ...".format(idx, smiles))
mol = ob.OBMol()
conversion.ReadString(mol, smiles)
mol.AddHydrogens()
N = mol.NumAtoms()
assert N == pos.size(0)
dist_min = calc_dist_min(mol, pos)
dist_min_ = calc_dist_min(mol, pos, ignore_bond=True)
logging.info("minimum interatomic distance with bonds: {:.4f} ...".format(dist_min))
logging.info("minimum interatomic distance without bonds: {:.4f} ...".format(dist_min_))
if dist_min is not None and dist_min < best_dist_min:
best_dist_min = dist_min
best_smiles = smiles
logging.info("Best dist min so far: {}, smiles is: {} ...".format(best_dist_min, best_smiles))
logging.info("Best dist min: {}, smiles is: {} ...".format(best_dist_min, best_smiles))
def main():
if args.dataset == "qm9":
qm9_filepath = "../dataset/QM9/raw/gdb9.sdf"
if args.mp:
statis_qm9_mp(qm9_filepath)
else:
statis_qm9(qm9_filepath)
else:
pcqm4m_filepath = "../dataset/pcqm4m_kddcup2021/raw/data.csv"
if args.mp:
statis_pcqm4m_mp(pcqm4m_filepath, subset_ratio=args.subset_ratio, start_idx=args.start_idx)
else:
statis_pcqm4m(pcqm4m_filepath, subset_ratio=args.subset_ratio, start_idx=args.start_idx)
if __name__ == "__main__":
args = parse_args()
if args.op == "check":
check_dist_min()
elif args.op == "statis":
main()
elif args.op == "contrast":
contrast_in_qm9()
elif args.op == "add":
supplement_pcqm4m()