Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model

Shatadru Majumdar; Soumik Kumar Nandi; Shuvam Ghosal; Bavrabi Ghosh; Writam Mallik; Nilanjana Dutta Roy; Arindam Biswas; Subhankar Mukherjee; Souvik Pal; Nabarun Bhattacharyya

doi:10.1007/s12559-021-09840-x

Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model

Cognit Comput. 2021 Feb 2:1-13. doi: 10.1007/s12559-021-09840-x. Online ahead of print.

Affiliations

¹ Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, India.
² Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, India.
³ Agri and Environmental Electronics (AEE), Centre for Development of Advanced Computing, Kolkata, India.

Abstract

To fight against the present pandemic scenario of COVID-19 outbreak, medication with drugs and vaccines is extremely essential other than ventilation support. In this paper, we present a list of ligands which are expected to have the highest binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used to make the drug for the novel coronavirus. Here, we implemented an architecture using 1D convolutional networks to predict drug-target interaction (DTI) values. The network was trained on the KIBA (Kinase Inhibitor Bioactivity) dataset. With this network, we predicted the KIBA scores (which gives a measure of binding affinity) of a list of ligands against the S-glycoprotein of 2019-nCoV. Based on these KIBA scores, we are proposing a list of ligands (33 top ligands based on best interactions) which have a high binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used for the formation of drugs.

Keywords: 1D CNN; Binding affinity; COVID-19; Drug–target interaction values; ECFP4; KIBA; Ligand; Protein Sequence Composition; S-glycoprotein.