Relational Graph Convolution Network with Multi Features for Anti- COVID-19 Drugs Discovery using 3CLpro Potential Target
Authors: Mswahili Medard Edmund , Ndomba Goodwill Erasmo , Kim Young Jin , Jo Kyuri , Jeong Young-Seob 
Published: March 2024

Background:

The potential of graph neural networks (GNNs) to revolutionize the analysis
of non-Euclidean data has gained attention recently, making them attractive models for deep machine
learning. However, insufficient compound or molecular graphs and feature representations might
significantly impair and jeopardize their full potential. Despite the devastating impacts of ongoing
COVID-19 across the globe, for which there is no drug with proven efficacy that has been shown to
be effective. As various stages of drug discovery and repositioning require the accurate prediction of
drug-target interactions (DTI), here, we propose a relational graph convolution network (RGCN) using
multi-features based on the developed drug compound-coronavirus target graph data representation
and combination of features. During the implementation of the model, we further introduced the
use of not only the feature module to understand the topological structure of drugs but also the structure
of the proven drug target (i.e., 3CLpro) for SARS-CoV-2 that shares a genome sequence similar
to that of other members of the betacoronavirus group such as SARS-CoV, MERS-CoV, bat coronavirus.
Our feature comprises topological information in molecular SMILES and local chemical context
in the SMILES sequence for the drug compound and drug target. Our proposed method prevailed
with high and compelling performance accuracy of 97.30% which could be prioritized as the
potential and promising prediction route for the development of novel oral antiviral medicine for COVID-
19 drugs.

Objective:

Forecasting DTI stands as a pivotal aspect of drug discovery. The focus on computational
methods in DTI prediction has intensified due to the considerable expense and time investment associated
with conducting extensive in vitro and in vivo experiments. Machine learning (ML) techniques,
particularly deep learning, have found broad applications in DTI prediction. We are convinced
that this study could be prioritized and utilized as the promising predictive route for the development
of novel oral antiviral treatments for COVID-19 and other variants of coronaviruses.

Methods:

This study addressed the problem of COVID-19 drugs using proposed RGCN with multifeatures
as an attractive and potential route. This study focused mainly on the prediction of novel antiviral
drugs against coronaviruses using graph-based methodology, namely RGCN. This research
further utilized the features of both drugs and common potential drug targets found in betacoronaviruses
group to deepen understanding of their underlying relation.

Results:

Our suggested approach prevailed with a high and convincing performance accuracy of
97.30%, which may be utilized as a top priority to support and advance this field in the prediction
and development of novel antiviral treatments against coronaviruses and their variants.

Conclusion:

We recursively performed experiments using the proposed method on our constructed DCCCvT
graph dataset from our collected dataset with various single and multiple combinations of features
and found that our model had achieved comparable best-averaged accuracy performance on T7
features followed by a combination of T7, R6, and L8. The proposed model implemented in this investigation
turns out to outperform the previous related works.