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Rabinarayan Panda

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Open access Jul 2026

Quantitative correlation of spectroscopic signatures with ligand–protein interactions in anti-cancer drug Afinitor: an integrated experimental–computational study

A detailed molecular-level understanding of anticancer drugs is essential for improving therapeutic efficacy and guiding rational drug design. Everolimus (Afinitor), a clinically important inhibitor of the mammalian target of rapamycin (mTOR) pathway, is widely used in cancer therapy; however, a quantitatively grounded relationship between its spectroscopic characteristics and ligand–protein interactions remains insufficiently explored. In this study, an integrated experimental–computational approach was employed, combining FT-IR spectroscopy, UV–Vis spectroscopy, and molecular docking simulations. Spectroscopic analyses were used to characterise functional groups and electronic structure, while docking simulations were performed to investigate interactions with FK506 binding protein (FKBP12) and the FKBP–rapamycin binding (FRB) domain. FT-IR analysis revealed a high density of oxygen-containing functional groups, including hydroxyl and carbonyl moieties, with vibrational frequencies indicative of a strongly polarised electronic environment. Molecular docking demonstrated favourable binding affinities with FKBP12 (− 9.7 and − 9.6 kcal·mol⁻1) and the FRB domain (− 8.5 and − 6.6 kcal·mol⁻1). Detailed interaction analysis showed that these functional groups correspond to specific interacting atoms (e.g., O66, O67, O63, and O36), forming quantifiable hydrogen bonds (1.7–2.9 Å) and electrostatic interactions (~ 4.37 Å) with key residues such as TYR82, THR85, and GLU54. The UV–Vis absorption maximum at 278 nm corresponds to a HOMO–LUMO energy gap of 4.46 eV, indicating moderate electronic polarizability that supports charge redistribution during binding. The study establishes a quantitative and mechanistically grounded structure–spectra–interaction relationship, demonstrating that spectroscopic observables encode the local electronic environment governing ligand–protein interaction propensity. Binding affinity is shown to arise from a cooperative network of multiple non-covalent interactions enabled by the spatial distribution of functional groups. This integrated framework provides predictive insight into drug–protein interactions and offers a robust foundation for the rational design and optimisation of mTOR-targeting therapeutics.

P. Ramana, Rashmirekha Ram, Prasadarao Bobbili et al. · 0 citations
Open access Jul 2026

A Hybrid Vision Mamba and Transformer Architecture for Offline Recognition of Handwritten Marathi Characters

Offline handwritten Marathi character recognition is still kind of hard research problem because there is so much variability within the same class ,and between classes they can look a bit similar ,also the strokes are complex and different people write in their own style. A lot of CNN and Transformer like methods either do not really capture long range relationships well enough, or they end up being too heavy computationally, you know not so efficient. So in this paper we suggest a Hybrid Vision Mamba and Transformer (HVMT), framework for stronger offline handwritten Marathi character recognition. The HVMT idea combines the hierarchical feature extraction power of Vision Mamba, which uses selective state-space modeling, with the contextual representation learning of a smaller Transformer encoder, and inside that encoder we use Multi-Head Self-Attention. Experiments are done on the public MHCD_GIETV2 dataset, where handwritten Marathi characters are collected from writers in different age groups and with diverse writing styles. Before training the images are turned into grayscale, then normalized, resized, and also augmented, to help the model generalize better. The proposed HVMT is compared with CNN, ResNet-50, EfficientNet-B0, ConvNeXt-Tiny, Vision Transformer (ViT-B/16), Swin Transformer-Tiny, and Vision Mamba, all under the same experimental setup. Experimental results show that the proposed framework achieved accuracy 87.11% , precision 87.09% , recall 87.10% and F1-score 87.09% which is better than the compared architectures. At the same time it only uses 26.9 million parameters, 2.6 GFLOPs, and inference time 1.305 ms per image. In other words, the HVMT framework seems to strike a workable tradeoff between recognition precision and compute efficiency. Because of this it is a good fit for things like intelligent document analysis, handwritten document digitization , archival preservation , and several other Indic script recognition tasks and more.

S. Khandakhani, Sachikanta Dash, Sasmita Padhy et al. · 0 citations

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