Official implementation of "Motif-based Graph Self-Supervised Learning forMolecular Property Prediction"

Related tags

Deep Learning MGSSL
Overview

Motif-based Graph Self-Supervised Learning for Molecular Property Prediction

Official Pytorch implementation of NeurIPS'21 paper "Motif-based Graph Self-Supervised Learning for Molecular Property Prediction" (https://arxiv.org/abs/2110.00987).

Requirements

pytorch                   1.7.0             
torch-geometric           1.6.3
rdkit                     2021.03.1
tqdm                      4.31.1
tensorboardx              1.6

To install RDKit, please follow the instructions here http://www.rdkit.org/docs/Install.html

  • motif_based_pretrain/ contains codes for motif-based graph self-supervised pretraining.
  • finetune/ contains codes for finetuning on MoleculeNet benchmarks for evaluation.

Training

You can pretrain the model by

cd motif_based_pretrain
python pretrain_motif.py

Evaluation

You can evaluate the pretrained model by finetuning on downstream tasks

cd finetune
python finetune.py

Cite

If you find this repo to be useful, please cite our paper. Thank you.

@article{zhang2021motif,
  title={Motif-based Graph Self-Supervised Learning for Molecular Property Prediction},
  author={Zhang, Zaixi and Liu, Qi and Wang, Hao and Lu, Chengqiang and Lee, Chee-Kong},
  journal={arXiv preprint arXiv:2110.00987},
  year={2021}
}
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Comments
  • [error] rdkit.Chem.rdchem.KekulizeException: Can't kekulize mol.

    [error] rdkit.Chem.rdchem.KekulizeException: Can't kekulize mol.

    Hello! I'm trying to run mol_tree.py, but I'm getting KekulizeException: error. Is it a problem about rdkit version? Below are my rdkit version and console output.

    rdkit=2021.09.2=py37h13c2175_0

    runfile('/home/bionuser/bionlab/bionlab/contrib/seyong/tasks/tmp/MGSSL/motif_based_pretrain/util/mol_tree.py', wdir='/home/bionuser/bionlab/bionlab/contrib/seyong/tasks/tmp/MGSSL/motif_based_pretrain/util') Python 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 06:08:53) Type 'copyright', 'credits' or 'license' for more information IPython 7.30.1 -- An enhanced Interactive Python. Type '?' for help. PyDev console: using IPython 7.30.1 Python 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 06:08:53) [GCC 9.4.0] on linux start CC(C)(C)c1ccc2occ(CC(=O)Nc3ccccc3F)c2c1 C[C@@H]1CC(Nc2cncc(-c3nncn3C)c2)CC@@HC1 N#Cc1ccc(-c2ccc(OC@@Hc3ccccc3)cc2)cc1 CCOC(=O)[C@@H]1CCCN(C(=O)c2nc(-c3ccc(C)cc3)n3c2CCCCC3)C1 N#CC1=C(SCC(=O)Nc2cccc(Cl)c2)N=C([O-])C@HC12CCCCC2 CCNH+C@(CC)C@Hc1cscc1Br COc1ccc(C(=O)N(C)C@@HC/C(N)=N/O)cc1O O=C(Nc1nc[nH]n1)c1cccnc1Nc1cccc(F)c1 Cc1c(/C=N/c2cc(Br)ccn2)c(O)n2c(nc3ccccc32)c1C#N Traceback (most recent call last): File "/home/bionuser/.conda/envs/bionlab/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3457, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "", line 1, in runfile('/home/bionuser/bionlab/bionlab/contrib/seyong/tasks/tmp/MGSSL/motif_based_pretrain/util/mol_tree.py', wdir='/home/bionuser/bionlab/bionlab/contrib/seyong/tasks/tmp/MGSSL/motif_based_pretrain/util') File "/home/bionuser/.pycharm_helpers/pydev/_pydev_bundle/pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "/home/bionuser/.pycharm_helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "/home/bionuser/bionlab/bionlab/contrib/seyong/tasks/tmp/MGSSL/motif_based_pretrain/util/mol_tree.py", line 149, in mol = MolTree(line) File "/home/bionuser/bionlab/bionlab/contrib/seyong/tasks/tmp/MGSSL/motif_based_pretrain/util/mol_tree.py", line 105, in init cmol = get_clique_mol(self.mol, c) File "/home/bionuser/bionlab/bionlab/contrib/seyong/tasks/tmp/MGSSL/motif_based_pretrain/util/chemutils.py", line 80, in get_clique_mol smiles = Chem.MolFragmentToSmiles(mol, atoms, kekuleSmiles=True) rdkit.Chem.rdchem.KekulizeException: Can't kekulize mol. Unkekulized atoms: 1 2 12 15 23

    Thank you!

    opened by rest1h 1
  • Questions about motifs

    Questions about motifs

    Hi, I wonder if there are ovelapping atoms between motifs in one molecule? I run the following script

    from moltree import MolTree
smiles = "O=C1[C@@H]2C=C[C@@H](C=CC2)C1(c1ccccc1)c1ccccc1"
mol = MolTree(smiles)
atom_count = 0
for c in mol.nodes:
    atom_count += c.mol.GetNumAtoms()
    print(c.mol.GetNumAtoms())
    print(c.clique)
print(f"atoms in original mol: {mol.mol.GetNumAtoms()}, atoms in motifs: {atom_count}")

    And the output is:

    2
[0, 1]
2
[9, 10]
2
[9, 16]
9
[1, 2, 3, 4, 5, 6, 7, 8, 9]
6
[11, 12, 13, 14, 15, 10]
6
[17, 18, 19, 20, 21, 16]
1
[9]
atoms in original mol: 22, atoms in motifs: 28
    opened by toooooodo 1
  • Where is the atom loss and bond loss?

    Where is the atom loss and bond loss?

    Dear author, in the paper the final loss is the sum of atom, bond and motif. I cannot find the loss of atom and bond in the source code. So what is the final loss function of MGSSL?

    opened by windyLemon 0
  • Are there some mistakes in tree decomposition modules?

    Are there some mistakes in tree decomposition modules?

    Dear author, I run the mol_tree.py to preprocess the dataset and generate clique.txt, but it throws the KekulizeException for molecules such as Cc1c(/C=C/c2cc(Br)ccn2)c(O)n2c(nc3ccccc32)c1C#N. I use the decomposition modules on other datsets, it make the same exceptions, can you update the code or give some advice?

    opened by 1004766790 1
Owner
zaixi
I am Zaixi Zhang from USTC
zaixi
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