Published December 26, 2020 | Version v2
Software Open

LigEGFR: Spatial graph embedding and molecular descriptors assisted bioactivity prediction of ligand molecules for epidermal growth factor receptor on a cell line-based dataset

Authors/Creators

  • 1. Kamnoetvidya Science Academy
  • 2. School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology
  • 3. Department of Biochemistry, Faculty of Science, Chulalongkorn University

Description

Source code for LigEGFR: predicting pIC50 and classifying hit compounds of ligands against human EGFR tyrosine kinase. The architecture was inspired and adapted from a convolution spatial graph embedding layer (C-SGEL) which was constructed by graph convolutional networks incorporating especial molecular descriptors.

  • LigEGFR_source.tar.gz for Anaconda-based installation (supported for Linux and macOS)
  • LigEGFR_docker.tar.gz for Docker-based installation  (supported for Windows, Linux and macOS)

For more information, please visit:

Preprint citation: https://doi.org/10.1101/2020.12.24.423424

GitHub: https://github.com/scads-biochem/LigEGFR

Files

Files (575.5 MB)

Name Size
md5:485bf32f8e0e4153709efb5e6e7dcf56
287.8 MB Download
md5:959abab7e71b16a2b3ba34a44213b896
287.8 MB Download

Additional details

Related works

Is documented by
Preprint: 10.1101/2020.12.24.423424 (DOI)