jeah-z / BDE-FGCN

BDE-FGCN : A fragments based model for predicting experimental bond dissociation energy

Fig. 1 Architecture of BDE-FGCN model

data format

Example:

SMILES,atom1,atom2,experimental_BDE,bond_type C/C=C\CCC,2,3,-0.5240901979171866,C-C

This code adopts SMILES as input with the index of two atoms in the rdkit. Above atomic index usually is the same as the index in the SMILES string.

Example:

SMILES,atom1,atom2,experimental_BDE,bond_type C/C=C\CCC,2,2,-0.5240901979171866,C-H

If the the bond involve implicit hydrogen, users could input the heavy atom's index twice, this script will detect implicit hydrogen index automatically.

to train the model

python model_H/train_qm.py --model sch_qm --saved_model pretrained_model --epochs 2000 --train_file dataset/full_train.csv --test_file dataset/full_valid.csv

to evals the model

python model_H/eval.py --model sch_qm --saved_model pretrained_model/model_400 --output ./application-OH.txt --test_file Dataset/Application_OH_GCN.csv

dependency

• rdkit
• dgl
• pytorch
• python==3.6
• numpy
• pandas
• zipfile
• os
• pathlib
• tqdm
• pathos
• argparse

related repository

This code was based on https://github.com/tencent-alchemy/Alchemy. If this script is of any help to you, please cite them.

• K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in Neural Information Processing Systems 30, pp. 992-1002 (2017) link

- @article{chen2019alchemy,
  title={Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models},
  author={Chen, Guangyong and Chen, Pengfei and Hsieh, Chang-Yu and Lee, Chee-Kong and Liao, Benben and Liao, Renjie and Liu, Weiwen and Qiu, Jiezhong and Sun, Qiming and Tang, Jie and Zemel, Richard and Zhang, Shengyu},
  journal={arXiv preprint arXiv:1906.09427},
  year={2019}
}