/Molormer

Molormer:A Lightweight Self-Attention-Based Method Focused on Spatial Structure of Molecular Graph for Drug-Drug Interactions Prediction

Primary LanguagePython

BIB | Molormer: A Lightweight Self-Attention-Based Method Focused on Spatial Structure of Molecular Graph for Drug-Drug Interactions Prediction

This repository contains the source code of Molormer (https://doi.org/10.1093/bib/bbac296).

Setup

A conda environment can be created with

conda create --name molormer python=3.7

conda activate molegent

conda update -n base conda

conda env create -f environment.yaml

Training

To train of the models run:

python train.py

Predicting

To prediction of the models run:

python predict.py

Cite:

Xudong Zhang, Gan Wang, Xiangyu Meng, Shuang Wang, Ying Zhang, Alfonso Rodriguez-Paton, Jianmin Wang, Xun Wang, Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug–drug interactions prediction, Briefings in Bioinformatics, 2022;, bbac296, https://doi.org/10.1093/bib/bbac296