I'm a computational biophysicist working at the intersection of molecular modelling, non-covalent interactions, machine learning, and computer-aided drug design. I hold a Ph.D. in Applied Physics, and my research connects physical insight about biomolecular recognition with computational and data-driven approaches.

I'm currently a Postdoctoral Fellow in the Department of Computational Biology at IIIT Delhi, working on AI/ML for ligand-based drug design, database development, and non-covalent interactions. Previously, I worked at the Hebrew University of Jerusalem on machine learning for enzyme catalytic activity prediction, protein electrostatics, and molecular modelling.

Research theme: combining molecular-level understanding with reproducible computational workflows to make biomedical predictions more useful and interpretable.

• 🧬 AI/ML for molecular science — protein language models, molecular embeddings, and graph-based learning.
• 💊 Computer-aided drug design — ligand-based modelling, docking, molecular dynamics, and binding interactions.
• ⚛️ Non-covalent interactions — understanding and quantifying the forces behind molecular recognition.
• 🧪 Scientific datasets — curation, preprocessing, and database development for catalytic activity and molecular property prediction.
• 🛠️ Research automation — Bash/Python workflows and reproducible computational chemistry and biology pipelines.

flowchart LR
    A[Scientific question] --> B[Curated molecular data]
    B --> C[Representations and features]
    C --> D[ML model development]
    D --> E[Cross-validation and evaluation]
    E --> F[Physical / chemical interpretation]
    F --> G[Reproducible research workflow]

• AI/ML for enzyme catalytic activity prediction — protein sequence representations, data curation, and model development.
• DNA–ligand interactions — computational modelling of recognition, dynamics, and stability.
• Non-covalent interactions in molecular systems — computational characterization of interaction patterns and cooperativity.
• Molecular cavitands and drug delivery — modelling host–guest complexes and preparing datasets for binding-affinity prediction.
• Computational drug discovery — molecular docking, molecular dynamics, and structure-based analysis of potential therapeutic compounds.

A few examples from my publication record:

1. High Throughput Meta-analysis of Antimicrobial Peptides for Characterizing Class Specific Therapeutic Candidates: An In-Silico Approach — Probiotics & Antimicrobial Proteins (2025).
2. Modelling DNA-ligand interactions through variable force-field based MD simulations — Indian Journal of Chemistry (2024).
3. Discerning Potent CSF-1R inhibitors for Targeting and Therapy of Neuroinflammation using Computational Approaches — Journal of Biomolecular Structure and Dynamics (2024).
4. Tracing the Transition from Covalent to Non-covalent Functionalization of Pyrene through C-, N-, and O-based Ionic and Radical Substrates using Quantum Mechanical Calculations — RSC Advances (2023).
5. Binding propensity and selectivity of cationic, anionic, and neutral guests with model hydrophobic hosts: A first principles study — Journal of Computational Chemistry (2023).

Alongside research, I enjoy teaching computational chemistry and biology, preparing hands-on training material, and mentoring students on molecular modelling, docking, simulation, and data-driven workflows. I have contributed to workshops and training programmes, including sessions on Python-based data mining and computer-aided drug discovery.

I'm interested in collaborations involving:

• AI/ML for molecular and protein science
• Ligand-based drug design and molecular interaction analysis
• Curated datasets for molecular property and enzyme activity prediction
• Computational chemistry, molecular dynamics, and reproducible research software