Bridging the antiviral drug design gap: a combined machine learning and QSAR approach for drug repurposing of host kinase inhibitors

Abstract

Viral outbreaks combined with rapid emergence of mutated viruses have highlighted an urging need for accelerating antiviral drug discovery pipelines. Unfortunately, current drug discovery remains stuck to conventional methods which are slow especially during pandemics. In this article, we present a literature-based synthesis of an integrative machine learning (ML) guided QSAR framework that unifies ligand-based, structure-based, and systems biology approaches towards the aim of generating a host directed antiviral repurposing strategy. Moreover, a modern ML enhanced QSAR modeling strategy is proposed to target host directed therapeutics (HDTs), particularly the host kinase enzymes. The proposed framework integrates molecular descriptor modeling, ensemble learning methods (e.g., RF, gradient boosting), graph neural networks (GNNs), and multi-omics target prioritization to outline a predictive antiviral repurposing model. This structured workflow encompasses dataset assembly, descriptor generation, model training, virtual screening, and experimental validation as sequential stages to guide, rather than as a pipeline that has itself been built or independently validated here, translational deployment. The review is illustrated through a retrospective narrative synthesis of four independently published, clinically relevant repurposed HDTs, namely Baricitinib, Lapatinib, Bemcentinib, and Sunitinib. These published case studies, drawn from the primary literature, exemplify how AI/ML-enhanced QSAR and network-based approaches have been used elsewhere to identify active antiviral kinase inhibitors; they are presented here as illustrative evidence of feasibility of such a computational pipeline. Thus, the AI guided repurposing of host kinase inhibitors offers a systematically accelerated strategy to bridge the drug design gap, with the potential for faster therapeutic deployment against viral threats pending prospective, harmonized validation. This review describes a framework that combines artificial intelligence (AI), machine learning (ML), and Quantitative Structure-Activity Relationship (QSAR) modeling to speed up the search for new antiviral drugs. Instead of targeting the virus directly, the framework targets host cell proteins such as kinases, which many viruses hijack during infection, an approach also known as host-directed therapy (HDT). To show how this approach could work, we review four drugs that were originally developed for other diseases and later found to also fight viral infections: Baricitinib, Lapatinib, Bemcentinib, and Sunitinib. Each case was reported independently in the published literature, and we present them here as examples of what AI-assisted drug repurposing can achieve, not as proof that our specific framework has itself been built and tested. Accelerated therapeutic antiviral drug discovery pipelines are being a critical need due to viral outbreaks and rapid emergence of mutated viruses. Host Directed Therapeutics (HDTs) are new drug discovery strategies that can modulate specific host pathways essential for viral multiplication. The AI-HDT Framework is proposed to bridge the gap between the computational chemical prediction and clinical real-life application. The integration of multi-omics data such as phosphoproteomic data and transcriptomic data using the GNN models will help scientist to identify uniquely expressed host genes during the various episodes of viral infection.