GRASP: Graph Reasoning Agents for Systems Pharmacology with Human-in-the-Loop

Omid Bazgir; Vineeth Manthapuri; Ilia Rattsev; Mohammad Jafarnejad

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arXiv · 2512.05502

GRASP: Graph Reasoning Agents for Systems Pharmacology with Human-in-the-Loop

Omid Bazgir·Vineeth Manthapuri·Ilia Rattsev·Mohammad Jafarnejad

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Abstract

Quantitative Systems Pharmacology (QSP) modeling is essential for drug development but it requires significant time investment that limits the throughput of domain experts. We present \textbf{GRASP} -- a multi-agent, graph-reasoning framework with a human-in-the-loop conversational interface -- that encodes QSP models as typed biological knowledge graphs and compiles them to executable MATLAB/SimBiology code while preserving units, mass balance, and physiological constraints. A two-phase workflow -- \textsc{Understanding} (graph reconstruction of legacy code) and \textsc{Action} (constraint-checked, language-driven modification) -- is orchestrated by a state machine with iterative validation. GRASP performs breadth-first parameter-alignment around new entities to surface dependent quantities and propose biologically plausible defaults, and it runs automatic execution/diagnostics until convergence. In head-to-head evaluations using LLM-as-judge, GRASP outperforms SME-guided CoT and ToT baselines across biological plausibility, mathematical correctness, structural fidelity, and code quality (\(\approx\)9--10/10 vs.\ 5--7/10). BFS alignment achieves F1 = 0.95 for dependency discovery, units, and range. These results demonstrate that graph-structured, agentic workflows can make QSP model development both accessible and rigorous, enabling domain experts to specify mechanisms in natural language without sacrificing biomedical fidelity.

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BibTeX RIS

Omid Bazgir, Vineeth Manthapuri, Ilia Rattsev, Mohammad Jafarnejad. 2025-12-05. GRASP: Graph Reasoning Agents for Systems Pharmacology with Human-in-the-Loop. https://arxiv.org/abs/2512.05502

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