Hendra virus (HeV) is a highly pathogenic zoonotic paramyxovirus that poses a serious threat to human and equine health, yet no approved antivirals or vaccines currently exist. RNA-dependent RNA polymerase (RdRp) of Hendra virus represents a critical and attractive target for antiviral drug development, given its essential role in both viral genome replication and mRNA transcription. Due to the lack of a human homolog, it is more druggable and less likely to cause host toxicity. Its sequence conservation among related paramyxoviruses further highlights its potential for the development of broad-spectrum inhibitors. This study offers the first comprehensive computational analysis of the Hendra virus RdRp, potentially promising FDA-approved drugs as possible inhibitors. A homology model of RdRp was generated in the absence of experimental three-dimensional (3D) structure, followed by virtual screening and molecular dynamics (MD) simulations to evaluate the drug binding and stability. Based on the highest energy, four FDA-approved drugs selected were menadiol diphosphate (−49.88 kcal/mol), masoprocol (−39.69 kcal/mol), pamidronic acid (−34.29 kcal/mol), and dinoprostone (−46.90 kcal/mol). Furthermore, these compounds exhibited significant interactions with the catalytic GDNE motif. With strong conformational stability and pharmacokinetic profile, masoprocol and menadiol diphosphate showed the most stable and energetically favorable interactions within the RdRp active site. These findings suggest their potential as repurposed therapeutic candidates against Hendra virus infection and they provide a structural basis for the development of broad-spectrum paramyxovirus inhibitors, justifying additional experimental confirmation.

Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in rare oncological conditions, and heterogeneous data fusion constraints. To address these challenges, we present a Quantum-Inspired Classical Convolutional Neural Network (QC-CNN) inspired by quantum analogies for automated bone cancer detection in radiographic images. The proposed architecture integrates classical convolutional layers for hierarchical feature extraction with a classical variational layer motivated by high-dimensional Hilbert space analogies for enhanced pattern discrimination. A curated and annotated dataset of bone X-ray images was utilized, partitioned into training, validation, and independent test cohorts. The QC-CNN was optimized using stochastic gradient descent (SGD) with adaptive learning rate scheduling, and regularization strategies were applied to mitigate overfitting. Quantitative evaluation demonstrated superior diagnostic performance, achieving high accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results highlight the ability of classical CNN with quantum-inspired design to capture non-linear correlations and subtle radiographic biomarkers that classical CNNs may overlook. This study establishes QC-CNN as a promising framework for quantum-analogy motivated medical image analysis, providing evidence of its utility in oncology and underscoring its potential for translation into clinical decision-support systems for early bone cancer diagnosis. All computations in the present study are performed using classical algorithms, with quantum-inspired concepts serving as a conceptual framework for model design and motivating future extensions.