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: Virakarin, Puri; Saengnil, Natthakan; Boonyarit, Bundit; Kinchagawat, Jiramet; Laotaew, Rattasat; Saeteng, Treephop; Nilsu, Thanasan; +3 Authors

doi: 10.5281/zenodo.4362561 , 10.5281/zenodo.4361641 , 10.5281/zenodo.4361642

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

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Abstract

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

Related Organizations

Vidyasirimedhi Institute of Science and Technology
Thailand
Chulalongkorn University
Thailand

Keywords

Graph embedding, Drug discovery, QSAR, EGFR, Machine learning, Deep learning, Epidermal Growth Factor Receptor

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