seongokryu/augmented-gcn

augmented-GCN

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This repository provides implementations introduced in "Deeply learning molecular structure property relationships using attention- and gate-augmented graph convolutional network".

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Usage

We used several scripts and a 'Harvard Clean Energy Project (CEP)' dataset in https://github.com/HIPS/neural-fingerprint.

1. First, make results and save directories to save output files and save files, respectively.

mkdir results mkdir save

2. Convert smiles files to graph inputs at a database folder.

cd database python smilesToGraph.py ZINC 10000 1 python smilesToGraph.py CEP 1000 1

3. Also, enter below command to obtain logP, TPSA, QED and SAS.

python calcProperty.py

4. Training

python train.py model property #layers #epoch initial_learning_rate decay_rate

python train.py GCN logP 3 100 0.001 0.95

models : GCN, GCN+a, GCN+g, GCN+a+g, GGNN

property : logP, TPSA, QED, SAS (ZINC dataset) and pve (CEP dataset)

augmented-gcn's Issues

Excuse me, I have a question about the version

Excuse me, because there were many problems related to the release (bug of the version, Contrib supported by the version, etc.) in the process of using, may I ask which version of RDKit you are using?

Pre-processing for PVE

Thank you for such a great work. Could you please tell me how did you pre-process photovoltaic efficiency for the CEP dataset?