Vidya Niranjan

MotifQu uses Grover's algorithm for motif discovery and scanning, showing feasibility on real genomes but limited amplitude concentration on current quantum hardware.

This paper presents a hybrid quantum-classical framework for peptide folding, demonstrating a workflow that outperforms quantum refinement in structural accuracy for a short peptide.

The paper identifies three natural compounds as potential FLT3 inhibitors for AML using computational methods including pharmacophore screening, docking, ADMET analysis, and molecular dynamics.

This study identifies phytocompounds that stabilize G4 structures in the hTERT promoter using in silico screening and molecular dynamics simulations.

This study identifies two natural compounds as potential DNMT3A inhibitors through computational screening and validation, suggesting their use in anti-leukemic therapies.

This study integrates metagenomic and metabolomic analyses to identify microbial communities and metabolites in mulberry soil, aiming to improve agricultural sustainability through biofertilizers and biopesticides.

The paper designs effective brefeldin A variants through combinatorial screening and virtual docking, leading to structural modification of EPAC2 and antibody design for target identification.

The paper identifies heat stress responsive long non-coding RNAs (lncRNAs) in banana and characterizes their expression patterns and potential functional roles.