Amritha Thaikkad

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.

Chandipura virus (CHPV) is a recently discovered neurotropic virus associated with severe encephalitis in children and for which there is currently no specific antiviral treatment. The viral RNA‐dependent RNA polymerase (RdRp) is an attractive antiviral target because of its essential role in viral RNA synthesis and the presence of conserved catalytic motifs required for polymerase function. In this study, a structure‐based computational approach was employed to investigate coumarin‐triazole derivatives as potential inhibitors targeting the conserved QGDNQ motif‐containing region of CHPV RdRp. A homology model of CHPV RdRp was generated and structurally evaluated, followed by prediction of the ligand‐binding pocket. A set of 80 coumarin‐triazole derivatives was subjected to extra‐precision (XP) molecular docking, and compounds 7a, 7b, and 7d were identified as potential candidates based on their docking scores and predicted binding free energies. Compound 7a showed a favorable docking score and interactions with the residues close to the catalytic motif, especially ASN705. The ADMET analysis also indicated favorable physicochemical and pharmacokinetic properties for the selected compounds. Molecular dynamics simulations were subsequently performed for 200 ns for both the protein‐only system and the ligand‐bound complexes to assess their structural stability and conformational behavior. RMSD and RMSF analysis revealed that Compound 7a exhibited comparatively lower residue fluctuations among the ligand‐bound systems. PCA indicated a more compact conformational distribution comparable to that observed for the remdesivir‐bound system. Post‐MD MM/GBSA analysis further supported the favorable binding of Compound 7a. Overall, the integrated computational analysis identified Compound 7a as a promising coumarin‐triazole candidate for further investigation against CHPV RdRp. Experimental studies are required to validate its antiviral activity and mechanism, and the findings provide a basis for further optimization of the coumarin‐triazole scaffold toward potential non‐nucleoside CHPV RdRp inhibitors.

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.

Human metapneumovirus (hMPV) primarily causes respiratory tract infections in young children and older adults. According to the 2024 Human Pneumonia Etiology Research for Child Health (PERCH) study, hMPV is the second leading common cause of pneumonia in children under five in Asia and Africa. The virus encodes nine proteins, including the essential Fusion (F) and G glycoproteins, which facilitate entry to the host cells. Currently, there are no approved vaccines or antiviral treatments for hMPV; supportive care is the primary way it is managed. Hence, this study focuses on the F protein as a therapeutic target to find a repurposable drug to fight hMPV. Refolding of the F protein and its binding to heparan sulfate enable hMPV infection. Heparin sulfate is important for hMPV binding, and we have found that cangrelor and AVN 944 can prevent the fusion of membranes. We developed a deep learning-based pharmacophore to identify potential drugs targeting hMPV, from which we could narrowed a list of 2400 FDA-approved drugs and 255 antiviral drugs to 792 and 72 drugs, respectively. We then conducted quantitative validation using the ROC curve. Further virtual screening of the drugs was performed, leading us to select the one with the highest docking score. The validation of the deep learning prediction in virtual screening Pearson correlation was done. Further, the MD simulation of these drugs confirmed that the protein-drug complex stability remained in dynamic condition. Further, the stability of protein-drug complexes than unbound protein was confirmed by Free Energy Landscape and Dynamic Cross Correlation Matrices. Further in vitro and in vivo experiments need to determine the efficacy of the identified candidates. Graphical abstract Our study shows that Cangrelor and AVN 944 can be repurposed against hMPV, which mainly affects young and older people.