MESPool: Molecular Edge Shrinkage Pooling for Hierarchical Molecular Representation Learning and Property Prediction

Fanding Xu, Zhiwei Yang, Lizhuo Wang, Deyu Meng and Jiangang Long

Pubilshed in Briefings in Bioinformatics

Requirements

torch = 1.11.0

torch_geometric = 2.0.4

torch_scatter = 2.0.9

torch_sparse = 0.6.13

torchmetrics = 0.7.3

Usage

There are three runnable Python scripts:

File	Description
run_egin.py	Run the benchmark test with EGIN framework.
run_mes.py	Run the benchmark test with MESPool framework.
visual_demo.py	Show the pooling result of a given molecule on the trained model.

You can use -h or --help to list all the available parameters in the scripts and their explanations, e.g:

python run_mes.py -h

All parameters in run_egin.py and run_mes.py have default settings, you can run them directly. The default benchmark dataset is BACE (also for visual_demo.py). You can change the task (dataset) by setting --dataset, e.g:

python run_mes.py --dataset bbbp

The available dataset options include: ['bbbp', 'bace', 'clintox', 'hiv', 'tox21', 'esol', 'freesolv', 'lipo', 'muv', 'sider', 'covid', 'muta'].

Notice: Before you start the benchmark test, please make sure the dataset has been put in ./datasets. You can download the benchmark we used in this work at https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv (replace the BBBP in the hyperlink with another dataset name to download other datasets).

visual_demo.py requires the SMILES and the name of molecule to be tested (the name is used for setting the file name of the pooling result image), e.g:

python visual_demo.py --smiles N1(C)C2=C(N(C)C(=O)N(C)C2=O)N=C1 --name Caffeine

If you have any questions, you can discuss with us in Issues.