Gram Matrix: An Efficient Representation of Molecular Conformation and Learning Objective for Molecular Pre-training

Environment Setup

You can follow the command to create the conda environment:

conda create --name GRAM --file requirements.txt

Or you can install the packages step by step. The following installation commands have been tested to be OK(2024/5/30):

conda create -n GRAM python==3.9
conda activate GRAM
conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia
conda install -c dglteam/label/th21_cu121 dgl
conda install pydantic
pip install rdkit
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.1.0+cu121.html
pip install scikit-learn
pip install dgllife
pip install MDAnalysis
pip install torch_geometric

Usage

Prepare dataset

1.GEOM dataset

step1 download data
Download rdkit_folder.tar.gz from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JNGTDF

step2 decompression the data

tar -zcvf rdkit_folder.tar.gz ./data/geom_drugs

step3 processing the dataset

python prepare_dataset_drugs.py

2.Fastsmcg dataset

Source: https://github.com/wangzhehyd/fastsmcg/tree/main
(dataset-1)
All processed files are avaliable at "./data/fastsmcg/processed.zip", you need to unzip it first.

3.Moleculenet dataset

Source: https://moleculenet.org/datasets-1
The raw dataset are alredy downloaded in data/moleculenet/

Pre-Training

python graphormer_geom_pretrain.py

Pre-Training model for Conformer prediction

step 1 Prepare dataset
You can refer to the steps in "Prepare dataset" to prepare your dataset

step 2 3D structure prediction
You should change the file path prepared first (example: the fastsmcg dataset path "./data/fastsmcg/processed")
Then run:

python 3d_prediction.py --path ./data/fastsmcg/processed/* --device cuda --batch_size 16

The RMSD will be printed when it finished.

property prediction model

1.Prepare data

python prepare_moleculenet.py  

2.Training (take Sider dataset for example)

python finetune_graphormer_sider.py

The trained models are avaliable at https://drive.google.com/drive/folders/1S9IzlOthWOiC5E9jxLgZALka7HdgNO90?usp=sharing