ArticleCognitive computation2021
Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug-Target Interaction Model.
Article in Cognitive computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- An Enhanced Computational Approach Using Multi-kernel Positive Unlabeled Learning for Predicting Drug-target Interactions.Current computer-aided drug design · 2025Article
- Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.Current drug targets · 2024Review
- Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network.Multimedia tools and applications · 2023Article
- A review of SARS-CoV-2 drug repurposing: databases and machine learning models.Frontiers in pharmacology · 2023Review
- Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design.Briefings in bioinformatics · 2021Article
- Computational identification of repurposed drugs against viruses causing epidemics and pandemics via drug-target network analysis.Computers in biology and medicine · 2021Article
- An Integrated Deep Learning and Molecular Dynamics Simulation-Based Screening Pipeline Identifies Inhibitors of a New Cancer Drug Target TIPE2.Frontiers in pharmacology · 2021Article
- Graph Neural Networks and Their Current Applications in Bioinformatics.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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