Deep learning-based prediction of drug-target interactions between antiviral drugs and SARS-CoV-2 proteins using an image-based representation approach.
Jul 2026· Computational biology and chemistry· Vol 125, pp.
109264
· 0 citations· 45 references
Medicine
TL;DR
This study applies MPS2IT-DTI (Molecule and Protein Sequence to Image Transformer for Drug-Target Interaction), a deep learning framework that represents molecular and protein sequences as images as images using k-mer frequency encoding, enabling convolutional neural networks to capture spatial compositional patterns associated with biochemical interactions.
Abstract
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.
Accurate identification of repurposable BCL-2 ligands requires not only plausible bound complex structures but also a dynamic description of how ligand binding reshapes residue-level communication. Here, we present a multimodal BCL-2 repurposing workflow built with diffusion-based generative modeling for ligand-specific complex generation and an extended neural relational inference (NRI) framework for trajectory-level interaction analysis. NeuralPlexer was applied to a library of 3094 FDA-approved drugs to generate BCL-2-ligand complex conformations at scale, yielding 1294 structurally acceptable complexes for downstream prioritization. To complement static scoring, filtered candidates were evaluated by molecular docking, anticancer QSAR classification, all-atom molecular dynamics (MD) simulations, and MM/GBSA binding free-energy calculations. We then extended NRI to protein–ligand trajectories to quantify residue-ligand and residue–residue dynamic couplings, enabling comparison of candidate-specific interaction signatures against the reference BCL-2 inhibitor Venetoclax. Among the prioritized compounds, Relugolix emerged as one of the most compelling hits, combining favorable binding energetics with an NRI-derived interaction pattern closely resembling that of Venetoclax. In vitro experiments supported BCL-2 inhibition by Relugolix in a TR-FRET assay and reduced viability of LN-18 glioma cells (IC50 = 23.55 μM). Together, these results establish a strategy that couples generative complex prediction with graph-based dynamic inference for structure-guided drug repurposing and identify Relugolix as a tractable scaffold for future BCL-2 inhibitor design.
Ehsan Sayyah, H. Tunc, A. Çelebi et al.· Journal of Chemical Informat...· 0 citations
Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets. This research presents Dual modality feature fused-drug target affinity (DMFF-DTA), a model for drug-target affinity anticipation using dual-modality neural networks that considers both the sequence and graph structure of medicines and proteins. To facilitate more exact and efficient drug-target interaction modeling, the model incorporates a binding site-focused graph generation method for extracting binding information. Experimental results show that DMFF-DTA is far more effective than current state-of-the-art approaches. By outperforming state-of-the-art approaches by more than 8%, the model demonstrates remarkable generalizability to hitherto unexplored medicines and targets. The model's biological relevance is confirmed by the model interpretability analysis. This paper presents a reliable and understandable method for improving computational drug discovery by integrating multi-view protein and drug properties.
Ghazala Sultan, J. Vincent, Ratna Sahaya et al.· International Conference Com...· 0 citations
Existing immunological datasets can support feature-constrained transfer learning for data-efficient prioritization of antibody:antigen interactions across closely related Sarbecoviruses, particularly when conserved epitope regions can be aligned and limited target-specific measurements are available for calibration.
Jimmy B. Yuan, Ryan Bruneau, Stephen Won et al.· Frontiers in Immunology· 0 citations
Accurate prediction of compound bioactivity is essential for accelerating antiviral drug discovery and reducing experimental costs. Machine learning (ML) methods have shown considerable promise in modeling structure–activity relationships and compound potency. In this study, we present an integrated ML framework for predicting IC50 and pIC50 values of compounds active against SARS-CoV-2, key indicators of antiviral potency. The proposed framework comprises three complementary approaches: (i) a regression model for quantitative IC50 prediction validated against experimental data; (ii) a classification model that categorizes compounds into active and inactive classes to support compound prioritization; and (iii) a multi-task neural network that jointly performs IC50 regression and activity classification, enhancing predictive performance and interpretability. A distinctive feature of this work is the incorporation of ligand efficiency (LE) as a criterion for activity classification, offering an alternative perspective on compound prioritization that has not been previously explored in SARS-CoV-2 bioactivity modeling. The proposed models demonstrate strong predictive capability, achieving a coefficient of determination (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}) of 0.77 using a neural network with feature selection, while the Random Forest classifier attains an accuracy, precision, and recall of approximately 0.92. These results highlight the potential of integrated regression, classification, and multi-task learning approaches as scalable and cost-effective tools for SARS-CoV-2 bioactivity prediction and antiviral drug discovery.
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Experimental results on multiple benchmark data sets demonstrate that MMU-DPI outperforms several state-of-the-art DPI prediction methods and indicate that MMU-DPI can serve as a useful computational tool for drug discovery.
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