What question did this study set out to answer?

The aim is to accelerate drug discovery by accurately predicting viral protein-ligand binding sites using deep learning.

January 25, 2026Open Access

ViralBindPredict: Empowering Viral Protein-Ligand Binding Sites through Deep Learning and Protein Sequence-Derived Insights

Puntos clave

The aim is to accelerate drug discovery by accurately predicting viral protein-ligand binding sites using deep learning.
Introduced a deep learning framework, ViralBindPredict, for binding site prediction.
Curated a large-scale benchmark of over 10,000 viral chains and around 13,000 interactions.
Utilized Mordred ligand descriptors and ESM2 or ProtTrans embeddings for context-aware predictions.
Implemented leakage-controlled data splits to enhance model reliability.
Multilayer perceptrons demonstrated stronger performance than LightGBM baselines, especially with ESM-2 embeddings.
ViralBindPredict maintained high precision-recall for unseen ligands but faced challenges with unseen proteins.
Accurate predictions of ligand-binding residues were achieved directly from protein sequences.

Resumen

Abstract Background The development of a single therapeutic compound can exceed 1.8 billion USD and take more than a decade, underscoring the urgent need to accelerate drug discovery. Computational methods have become indispensable; however, traditional approaches, such as docking simulations, face limitations because they depend on protein and ligand structures that may be unavailable, incomplete, or of low accuracy. Even recent breakthroughs, such as AlphaFold, do not consistently provide models precise enough to identify ligand-binding sites or drug–target interactions. Results We present ViralBindPredict, a deep learning framework that predicts viral protein–ligand binding sites directly from sequence. We also introduce the first curated large-scale benchmark of viral protein–ligand interactions, comprising 10,000 viral chains and ≈13,000 interactions processed using a 4.5 Å heavy-atom contact threshold. ViralBindPredict combines Mordred ligand descriptors with contextual protein embeddings from ESM2 or ProtTrans, enabling structure-free learning of binding preferences. Leakage-controlled data splits were applied to prevent overlap across protein sequence clusters and ligand scaffolds (Cluster90%, NoRed90%→Cluster90%, Cluster40%, NoRed90%→Cluster40%). Across most regimes, multilayer perceptrons, especially with ESM-2 embeddings, outperformed LightGBM baselines, maintaining strong precision–recall for unseen ligands but showing larger drops for unseen proteins, indicating that the protein context dominates generalization. Conclusions ViralBindPredict introduces the first leakage-controlled benchmark for viral protein–ligand interactions and demonstrates accurate ligand-binding residue prediction directly from protein sequence. Together, these advances establish ViralBindPredict as a robust and extensible workflow for sequence-based antiviral discovery, supporting rapid target prioritization, compound repurposing, and de novo drug design, even in the absence of structural data.

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