Rohit Jadhav PhD
I build systems that turn large-scale omics data into drug targets, biomarkers, and clinical insights — and increasingly, I build the AI agents that run those systems autonomously. 15+ years at the intersection of computational biology and translational medicine, spanning epigenomics, immuno-oncology, and multi-agent AI for drug discovery.
Bridging Biology and AI
at Scale
My work sits at a rare intersection: I understand the biology deeply enough to ask the right questions, and I build the computational infrastructure to answer them at scale. Over 15 years, that has meant everything from ATAC-seq pipelines for T-cell exhaustion to multi-agent LLM systems that evaluate thousands of drug targets in days.
At Juvena Therapeutics, I lead informatics and ML applications across the platform — establishing cloud-native NGS infrastructure on GCP, building agentic AI workflows in partnership with Eli Lilly, and identifying multi-omics biomarkers that advanced a lead molecule into a Phase 1 clinical trial.
I publish in Nature, PNAS, and Cancer Research, serve as a reviewer for Bioinformatics and Scientific Reports, and maintain active open-source pipelines used across the field.
Agentic AI for Drug Discovery
Multi-agent LLM workflows, RAG, vector DBs, knowledge graphs — built for target prioritization at scale
Multi-Omics Biomarker Discovery
RNA-seq, scRNA-seq, proteomics, ATAC-seq, CRISPR screening — integrated for clinical translation
Cloud-Scale Infrastructure
Nextflow pipelines on GCP (Cloud Run, BigQuery, Vertex AI), Docker, GitHub Actions
Areas of Deep Focus
Where biology, computation, and AI intersect to drive translational impact.
Agentic AI for Drug Discovery
Building multi-agent LLM systems that autonomously synthesize literature, score pathway evidence, and prioritize drug targets at scales impossible for human teams alone. RAG pipelines, vector databases, knowledge graphs, and tool-using agents — designed for production deployment in biopharma.
Multi-Omics Integration
Integrating RNA-seq, scRNA-seq, proteomics, ATAC-seq, and CRISPR screening data to identify biomarkers, therapeutic targets, and mechanisms of disease. End-to-end: QC, alignment, differential analysis, pathway enrichment, visualization, and clinical reporting.
Immuno-Oncology & T-Cell Biology
Deep expertise in T-cell exhaustion, PD-1 blockade response, epigenetic regulation of immune aging, and tumor-immune interactions. Computational work directly tied to published clinical and preclinical discoveries in checkpoint immunotherapy.
Cloud-Native Bioinformatics
Designing and operating production-grade bioinformatics infrastructure: Nextflow pipelines on GCP, containerized workflows, CI/CD with GitHub Actions, cost-optimized cloud compute, and scalable data management with BigQuery and Cloud Storage.
What I've Built
Selected projects that demonstrate the intersection of AI, biology, and translational impact.
Large-Scale Gene Target Evaluation Platform
Built a multi-agent LLM system to evaluate thousands of gene targets for therapeutic relevance in aging and metabolic disease. The system autonomously synthesized literature, scored pathway evidence across multiple data modalities, and ranked candidates — compressing a multi-year manual effort into days.
Multi-Omics Biomarker Discovery → Phase 1 Trial
Integrated plasma proteomics, bulk RNA-seq, and CRISPR functional screening to identify pharmacodynamic and efficacy biomarkers for a lead therapeutic candidate. Biomarkers are now in active clinical use supporting patient stratification and dose optimization.
Epigenetic Characterization of CD8 T-Cell Exhaustion
Defined chromatin accessibility signatures distinguishing progenitor-exhausted from terminally exhausted CD8 T cells in chronic infection — and showed these populations respond differentially to PD-1 blockade. This ATAC-seq work provided mechanistic insight into why checkpoint immunotherapy works in some patients and not others.
Selected Research
Peer-reviewed work spanning immuno-oncology, epigenomics, and computational biology.
Writing
Thoughts on agentic AI, computational biology methods, and the future of AI-driven drug discovery.
Posts coming soon — writing in progress.
Let's Talk
Open to conversations about computational biology, agentic AI in drug discovery, and collaboration opportunities. Based in Fremont, CA — available for Bay Area and remote roles.