Deep Learning for Protein-Ligand Binding Affinity Prediction in Antiviral Drug Design

Introduction

Accurate prediction of protein-ligand binding affinity is a cornerstone of computational antiviral drug design [1]. The ability to rank small-molecule inhibitors by their binding strength against viral protein targets directly influences the efficiency of virtual screening campaigns and lead optimization pipelines [1]. In veterinary virology, such computational tools are increasingly applied to identify novel therapeutics against economically significant pathogens including avian influenza virus, porcine coronaviruses, and African swine fever virus. Traditional structure-based methods, such as molecular docking and molecular mechanics generalized Born surface area (MM/GBSA) rescoring, have been supplemented (and in some cases supplanted) by deep learning architectures that learn complex biophysical features from large structural and affinity datasets [1]. This article reviews the current state of deep learning for protein-ligand binding affinity prediction with a specific focus on antiviral applications relevant to animal health.

Biophysical Basis of Binding Affinity

Protein-ligand binding affinity is quantified by the equilibrium dissociation constant (Kd) or the half-maximal inhibitory concentration (IC50), which reflects the Gibbs free energy of binding (ΔGbind) [1]. The physical determinants of ΔGbind include van der Waals interactions, electrostatic complementarity, hydrogen bonding, desolvation penalties, and conformational entropy changes upon complex formation [1]. Classical scoring functions approximate these terms using empirically weighted energy functions, whereas deep learning models learn the mapping from structural or sequence representations to experimental affinity values directly from training data [1]. The choice of representation (3D atomic coordinates, 2D graphs, or 1D sequences) critically affects model performance and generalizability across viral targets [1].

Deep Learning Architectures for Affinity Prediction

Graph Neural Networks (GNNs)

GNNs have emerged as the dominant architecture for structure-based affinity prediction because they naturally encode the 3D geometry of protein-ligand complexes as graphs, where nodes represent atoms and edges represent interatomic bonds or spatial proximity [1]. Convolutional operations on these graphs capture local and global interaction patterns. Liu et al. systematically compared GNN-based models with classical docking and MM/GBSA approaches for predicting binding poses and affinities of inhibitors targeting coronavirus main proteases [1]. Their study demonstrated that GNNs outperformed physics-based scoring functions in ranking ligand potencies, particularly when training data spanned multiple protease variants [1]. However, classical methods provided superior pose prediction for geometrically constrained active sites [1].

Transformer-Based Models

Transformers, originally developed for natural language processing, have been adapted to protein-ligand binding prediction by treating atomic or residue sequences as tokenized inputs [1]. Attention mechanisms enable the model to weight pairwise interactions across distant regions of the binding interface. In the context of antiviral design, transformer architectures have been applied to predict affinity changes resulting from single-point mutations in viral proteases, a scenario critical for forecasting drug resistance [1].

Hybrid Approaches

Hybrid models combine deep learning with physics-based terms to leverage the strengths of both paradigms. For example, a neural network can be trained to correct the systematic errors of a classical scoring function, or a graph network can incorporate electrostatic and van der Waals features computed from molecular mechanics force fields [1]. Liu et al. observed that such hybrid models achieved the highest correlation with experimental IC50 values for coronavirus main protease inhibitors, although the improvement over pure GNNs was modest [1].

Key Databases and Benchmarks

The development and validation of affinity prediction models rely on curated databases of experimentally determined binding data. The most widely used resources include the following:

Liu et al. employed a curated subset of PDBbind and BindingDB entries specific to coronavirus main proteases, including the 3CLpro enzyme from feline coronavirus and porcine epidemic diarrhea virus (PEDV), to train and evaluate their comparative models [1]. The study emphasized that database coverage for veterinary viral targets remains sparse compared to human pathogens, which limits model transferability [1].

Structure-Based versus Sequence-Based Approaches

Structure-based methods require high-quality 3D structures of the viral target, typically obtained from X-ray crystallography or cryo-electron microscopy. Deep learning models that operate on 3D atomic coordinates (e.g., GNNs, 3D convolutional neural networks) can exploit detailed geometric features such as hydrogen bond geometry and hydrophobic contact patterns [1]. Sequence-based methods, in contrast, use only amino acid and ligand SMILES strings, predicting affinity without explicit structural information. While sequence-based models are more generalizable to targets without solved structures, they lack the spatial resolution needed for accurate ranking of close analogs [1]. Liu et al. reported that for coronavirus main proteases, structure-based GNNs systematically outperformed sequence-based transformers on affinity ranking tasks, but the sequence model was less sensitive to conformational variation induced by crystallization conditions [1].

Case Studies in Antiviral Drug Design

Influenza Neuraminidase

Influenza neuraminidase (NA) is a validated drug target in veterinary medicine, with inhibitors such as oseltamivir used to treat avian influenza infections in poultry and swine. Deep learning affinity prediction models have been applied to identify novel NA inhibitors that overcome resistance mutations, particularly the H274Y substitution in N1 neuraminidase [1]. Liu et al. did not directly evaluate NA, but their methodological framework can be adapted because the same GNN architectures are transferable across viral enzyme classes [1]. For detailed molecular dynamics simulations of avian influenza hemagglutinin, readers are referred to the relevant Deep Learning-Driven Structural Prediction of Avian Influenza Hemagglutinin and Molecular Dynamics Simulations of Influenza Hemagglutinin articles.

Coronavirus Main Protease (3CLpro)

The coronavirus 3CLpro enzyme is essential for viral polyprotein processing and represents a high-value target for broad-spectrum antiviral design across veterinary coronaviruses including PEDV, transmissible gastroenteritis virus (TGEV), and canine respiratory coronavirus. Liu et al. conducted their comparative study using a set of 3CLpro inhibitors with experimentally determined IC50 values [1]. GNN models trained on these data achieved rank correlation coefficients (Spearman's ρ) exceeding 0.75 on held-out test sets, compared to 0.55 for classical docking scores [1]. The study also highlighted that deep learning predictions were more robust to changes in the protonation state of catalytic cysteine residues, a common source of error in physics-based calculations [1]. For an interactive visualization of 3CLpro-inhibitor complexes, the reader is encouraged to consult the 3D Protein Viewer linked in the accompanying Protein-Ligand Docking and Molecular Dynamics Simulations and Structure-Based Drug Design in Bioinformatics articles.

Workflow for Deep Learning-Based Affinity Prediction in Antiviral Drug Design

The following Mermaid diagram summarizes a typical computational pipeline for applying deep learning to predict binding affinity, from target selection to hit validation.

flowchart TD
    A[Viral Target Identification<br>e.g., 3CLpro, NA], > B[Structure Acquisition<br>X-ray, Cryo-EM, or AlphaFold Model]
    B, > C[Database Curation<br>PDBbind, BindingDB, ChEMBL]
    C, > D[Feature Engineering<br>Graph Construction for GNN]
    D, > E[Model Training<br>GNN / Transformer / Hybrid]
    E, > F[Virtual Screening<br>Large Compound Library]
    F, > G[Affinity Ranking & Hit Selection]
    G, > H[In Vitro & In Vivo Validation]
    H, > I{Validated?}
    I, >|Yes| J[Lead Optimization]
    I, >|No| D
    J, > K[Preclinical Candidate]

The pipeline begins with the selection of a viral protein target and the retrieval or prediction of its 3D structure. Next, experimental binding data for known inhibitors are curated from structural and affinity databases. Deep learning models, typically GNNs, are trained on these data and then applied to score a library of candidate compounds. Top-ranked hits are advanced to experimental testing. This workflow, as implemented by Liu et al. for coronavirus 3CLpro, reduces the number of compounds requiring wet-laboratory evaluation by several orders of magnitude [1].

Limitations and Future Directions

Despite considerable progress, several challenges remain. First, the quality and quantity of affinity data for veterinary viral targets are limited; most experimental measurements are concentrated on human pathogens [1]. Second, deep learning models can overfit to the specific chemical space of the training set, leading to poor generalization to novel scaffolds [1]. Third, dynamic effects such as induced fit and allosteric modulations are not captured by static crystal structures, and incorporating conformational ensembles remains computationally demanding [1]. Emerging solutions include the use of AlphaFold-predicted structures as input (see AlphaFold and Beyond: Deep Learning for Protein Structure Prediction in Veterinary Virology and AlphaFold 3 in Molecular Biology), equivariant neural networks that respect physical symmetries, and multi-task learning frameworks that jointly predict affinity, binding pose, and solvation free energies [1]. Continued investment in high-throughput experimental screening against veterinary-specific viral targets will be essential for expanding the applicability of these computational methods.

Conclusion

Deep learning, particularly graph neural networks and transformer architectures, has markedly improved the accuracy of protein-ligand binding affinity prediction for antiviral drug design [1]. Comparative studies on coronavirus main proteases demonstrate that GNN-based models surpass classical docking scores in ranking inhibitor potencies, while hybrid approaches that combine learned and physical terms offer the best balance of accuracy and robustness [1]. The integration of these models into virtual screening workflows accelerates the discovery of lead compounds against animal viral pathogens. However, the field must address data scarcity for veterinary targets and improve generalization to uncharted chemical space. Cross-linking to other computational virology resources, such as Deep Learning for Predicting Antiviral Resistance Mutations in Influenza Neuraminidase and Computational Modeling of Viral Polymerase Complexes for Antiviral Drug Discovery, provides additional context for the practical application of these techniques.

References

[1] Liu Y, Tang H, Niu T, et al. A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein-Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases. J Chem Inf Model. 2026. https://pubmed.ncbi.nlm.nih.gov/41429653/ *** Disclaimer This article is for educational and informational purposes only. It is not intended to substitute for professional veterinary advice, diagnosis, treatment, or regulatory guidance. Always consult a licensed veterinarian or qualified specialist regarding animal health, disease diagnosis, and therapeutic decisions.