Double-head Transformer Neural Network for Molecular Property Prediction

This is a Pytorch implementation of Double-head Transformer Neural Network for Molecular Property Prediction (DHTNN)

Figure 1 Overall DHTNN architectural diagram

Environment

python=3.8.10
pytorch=1.4.0
torchvision=0.5.0
chemprop=1.3.0
flask=2.0.1
hyperopt=0.2.5
matplotlib=3.5.1
numpy=1.20.3
pandas=1.2.4
pandas-flavor=0.2.0
pip=21.1.1
rdkit=2021.03.2
scikit-learn=0.24.2
scipy=1.6.3
tensorboardX=2.2
tqdm=4.61.0
typed-argument-parser=1.6.2
git+https://github.com/bp-kelley/descriptastorus

Dataset

There are six datasets, which are Lipophilicity, PDBbind, PCBA, BACE, Tox21, and SIDER.

You can be get the data by uncompressing data.zip. All the data used in the experiments is in here.

Train and Test the model

chemprop_train --data_path <path> --dataset_type <type> --save_dir <dir>

where <path> is the path to a CSV file containing a dataset, <type> is one of [classification, regression] depending on the type of the dataset, and <dir> is the directory where model checkpoints will be saved.

Acknowledgement

We refer to the paper of Analyzing Learned Molecular Representations for Property Prediction. We are grateful for the previous work of swansonk14 team.