Deep learning-based prediction of drug–target interactions between antiviral drugs and SARS-CoV-2 proteins using an image-based representation approach

This is the authors' abstract. We don't add key points for this paper.

Drug repurposing offers a time-efficient strategy for identifying therapeutics against emerging pathogens such as SARS-CoV-2. In this study, we apply MPS2IT-DTI (Molecule and Protein Sequence to Image Transformer for Drug-Target Interaction), a deep learning framework that represents molecular (SMILES) and protein (FASTA) sequences as images using k-mer frequency encoding, enabling convolutional neural networks to capture spatial compositional patterns associated with biochemical interactions. A curated dataset (BindingDB-FDA) containing 83,165 binding interactions from 1640 FDA-approved ligands and 3270 targets was constructed from BindingDB, with binding scores derived from the KIBA scoring system. An enhanced variant, MPS2IT+MN, incorporating max-norm regularization, was introduced to improve generalization. The model was applied to predict binding affinities between 33 FDA-approved antiviral drugs and six key SARS-CoV-2 non-structural proteins. Results consistently identified five antivirals - MK-5172 (Grazoprevir), Simeprevir, Lopinavir, Etravirine, and Atazanavir - as top-ranked candidates across all targets. Comparative analysis with the MT-DTI model demonstrated competitive and, in several cases, superior ranking performance despite a simpler architecture. Importantly, these predictions are supported by independent experimental and clinical evidence, highlighting the potential of image-based representations as a computationally efficient and biologically meaningful approach for drug-target interaction prediction and drug repurposing.