Hello, I’m Pritam 👋

I’m a bioinformatician and AI research scientist at Stanford University School of Medicine, working where deep learning, molecular modeling, and scientific software meet. My research focuses on AI-driven protein design and computational drug discovery—including safer anesthetic candidates for field and battlefield medicine.

I build end-to-end research systems: from foundation models, graph neural networks, and molecular simulation to reproducible pipelines, evaluation frameworks, and tools scientists can actually use. Over 8+ years, my work has crossed computational biology, clinical genomics, high-performance computing, and open-source engineering.

Research north star: compress the distance between a promising biological idea and a testable therapeutic hypothesis.

What I’m exploring now

• Generative and predictive models for protein structure and function
• Structure-based drug design, molecular docking, and ADMET intelligence
• Biomedical knowledge graphs, RAG, and evidence-grounded scientific agents
• Reproducible, scalable pipelines for multi-omics and high-throughput discovery
• Honest benchmarks that distinguish model performance from scientific utility

Flagship open-source work

🐼 PandaDock Physics-based molecular docking with flexible-ligand search and SE(3)-equivariant GNN rescoring. Research signal: Pearson R = 0.88 on the PDBbind refined set; reproducible search, rich interaction analysis, and publication-ready reports.	🐼 PandaMap Protein AND ligAnd interaction MAPper — comprehensive detection, visualization, and empirical binding affinity estimation for protein–ligand complexes. Design goal: protein-ligand interactions visualization tool.
🧪 ADMET Intelligence Graph neural networks predict molecular ADMET properties while a RAG-based explainer connects predictions to mechanistic context. GNN RAG Molecular ML Explainability	🔎 Drug Repurposing Intelligence R-GCNs and biomedical knowledge graphs uncover drug–disease relationships, paired with evidence-aware natural-language explanations. Knowledge Graphs R-GCN NLP RAG
🎯 Oncology Drug Response Deep-learning clinical decision support for predicting cancer cell-line drug response with context-specific RAG explanations.	📚 Literature Intelligence Tracks and analyzes PubMed and bioRxiv literature for drug targets, compounds, and therapeutic areas.

Explore all repositories →

Research-to-software pipeline

flowchart LR
    A[Biological question] --> B[Curated data]
    B --> C[Representation learning]
    C --> D[Predictive or generative model]
    D --> E[Physics-aware validation]
    E --> F[Reproducible workflow]
    F --> G[Testable therapeutic hypothesis]
    G -. experimental evidence .-> A

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Technical ecosystem

AI & scientific computing

Scientific workflows & infrastructure

Molecular & biomedical systems

GitHub analytics

Deep-dive metrics dashboard

Contribution map

Selected scholarship & impact

• Published work spanning AI-enabled drug design, molecular modeling, clinical genomics, nanotherapeutics, and biomolecular simulation.
• Research experience across Stanford University, Uppsala University, Karolinska Institutet, DKFZ, and University Medical Center Freiburg.
• Nextflow Ambassador and Sigma Xi member; builder of educational resources in AI-driven drug discovery, NGS, quantum chemistry, and molecular dynamics.
• Recipient of research and innovation support from Colgate-Palmolive, Uppsala University/ABB–Hitachi, and Karolinska Institutet.

Let’s build something consequential

I’m open to research collaborations, translational partnerships, and open-source work in:

• AI-driven protein and therapeutic design
• Computational biology and multi-omics infrastructure
• Foundation models and knowledge systems for biology
• Reproducible scientific software and benchmarking

_{Science moves faster when the tools, evidence, and ideas are open.}