AI/ML · Builder
Biotech Accelerator.
Multi-agent AI system for biotech research that routes a query through protein databases, literature, structural analysis, and chemistry, then proposes experiments worth running next.
- Role
- Builder
- When
- 2026
- Stack
- Python, LangGraph, ProDy, httpx
- Scale
- 5 agents in the pipeline
LangGraph · UniProt · PDB · ChEMBL5 agentsin the pipeline
4 sourcesexternal APIs
100NMA modes
Hexagonalports + adapters
The problem
Early-stage biotech research means stitching together protein databases, literature, structural models, and chemistry by hand before anyone can decide which experiment to run. The work is slow, easy to get wrong, and hard to reproduce.
What it does
- A LangGraph-orchestrated pipeline that parses a research query and routes it through UniProt, PubMed, the PDB, normal-mode analysis (ANM/GNM), and ChEMBL in sequence.
- Each external source sits behind its own agent with a typed interface, so a failed lookup degrades gracefully instead of breaking the run.
- Normal-mode analysis via ProDy adds a structural/dynamics signal on top of sequence and literature evidence, which most query tools skip.
- Packaged in Docker for a reproducible run, with a Rich terminal UI for readable progress and output.
Impact
- Turns a multi-tool manual research loop into one query that returns a structured list of suggested experiments.
- Cross-references computational evidence with literature so suggestions are grounded, not just generated.
- Reproducible end to end: the same query and container produce the same pipeline, useful for handing results to a collaborator.