We study how genes, proteins, and cells work together, and what goes wrong in disease. Using AI, structural biology, and multi-scale data, we build tools that address India's most pressing health challenges.
We work at the crossroads of structural biology, machine learning, and systems biology. Starting from protein structures and DNA sequences, we trace how molecular changes drive disease, then use AI and multi-omics data to find targets for treatment. Our focus is on the diseases most relevant in India: heart disease, neurodegeneration, blood disorders, and cancer. The goal is science that is rigorous at the bench and translatable at the bedside.
Understanding how proteins move, fold, and interact at atomic resolution, using molecular dynamics and structural modeling.
3 projects →Building AI systems that learn the rules behind effective drugs, and can explain those rules to scientists.
2 projects →Combining genomic, transcriptomic, and clinical data to find disease signatures, predict patient risk, and automate diagnostics. Clinical partner: PGIMER, Chandigarh.
4 projects →
Dr. Monika Sharma will serve as a resource person for a two-day workshop on computational oncology, organized by CEMB, SSSIHL, Puttaparthi, in collaboration with PHCCO, IISc Bengaluru.
Plaksha University will organize ConfBioTech on 21 August 2026.
Megha presented a poster at the ENCALS Meeting 2026 in Madrid, Spain, held from 23 to 26 June.
Multi-omic machine learning overcomes transcriptomic blind spots to rescue therapeutic targets in ALS
Automated Bone Marrow Cellularity Estimation from Whole-Slide Images Using Deep Learning: Validation in an Indian Clinical Cohort
Functional consequences of genetic variations in DgoR, a GntR/FadR family transcriptional repressor of D-galactonate metabolism in Escherichia coli