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

• URL: http://arxiv.org/abs/2512.05502v1
• Date: Fri, 05 Dec 2025 07:59:16 GMT
• Title: GRASP: Graph Reasoning Agents for Systems Pharmacology with Human-in-the-Loop
• Authors: Omid Bazgir, Vineeth Manthapuri, Ilia Rattsev, Mohammad Jafarnejad,
• Abstract summary: We present textbfGRASP -- a multi-agent, graph-reasoning framework with a human-in-the-loop conversational interface.<n>It encodes QSP models as typed biological knowledge graphs and compiles them to executable/Sim code while preserving units, mass balance, and physiological constraints.<n>It outperforms SME-guided CoT and ToT baselines across biological plausibility, mathematical correctness, structural fidelity, and code quality.
• Score: 0.6019777076722421
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Mozi: Governed Autonomy for Drug Discovery LLM Agents [21.429647382651677]
  In dependency-heavy pharmaceutical pipelines, autonomous agents often drift into irreproducible trajectories.<n>We present Mozi, a dual-layer architecture that bridges the flexibility of generative AI with the deterministic rigor of computational biology.<n>We demonstrate Mozi's ability to navigate massive chemical spaces, enforce stringent toxicity filters, and generate highly competitive in silico candidates.
  arXiv  Detail & Related papers  (2026-03-04T02:22:21Z)
• Physiologically Informed Deep Learning: A Multi-Scale Framework for Next-Generation PBPK Modeling [5.007023403094322]
  We propose a unified Scientific Machine Learning (SciML) framework that bridges mechanistic rigor and data-driven flexibility.<n>We introduce three contributions: (1) Foundation PBPK Transformers, which treat pharmacokinetic forecasting as a sequence modeling task; (2) Physiologically Constrained Diffusion Models (PCDM), a generative approach that uses a physics-informed loss to synthesize biologically compliant virtual patient populations; and (3) Neural Allometry, a hybrid architecture combining Graph Neural Networks (GNNs) with Neural ODEs to learn continuous cross-species scaling laws.
  arXiv  Detail & Related papers  (2026-02-09T00:26:01Z)
• Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement [66.51979814832332]
  model formulates procedural graph extraction as a multi-round reasoning process with dedicated structural and logical refinement.<n>Experiments demonstrate that model achieves substantial improvements in both structural correctness and logical consistency over strong baselines.
  arXiv  Detail & Related papers  (2026-01-27T04:00:48Z)
• Med-CRAFT: Automated Construction of Interpretable and Multi-Hop Video Workloads via Knowledge Graph Traversal [13.216513001286812]
  textbfPipelineName is a novel neuro-symbolic data engineering framework.<n> Med-CRAFT extracts structured visual primitives from raw video streams and instantiates them into a dynamic Spatiotemporal Knowledge Graph.<n>We instantiate this pipeline to produce M3-Med-Auto, a large-scale medical video reasoning benchmark.
  arXiv  Detail & Related papers  (2025-11-30T19:24:10Z)
• Migration as a Probe: A Generalizable Benchmark Framework for Specialist vs. Generalist Machine-Learned Force Fields [1.572216094651749]
  Machine-learned force fields (MLFFs) are transforming computational materials science by enabling ab initio-level accuracy at molecular dynamics scales.<n>Yet their rapid rise raises a key question: should researchers train specialist models from scratch, fine-tune generalist foundation models, or use hybrid approaches?<n>We introduce a benchmarking framework using defect migration pathways, evaluated through elastic band trajectories, as diagnostic probes.<n>Fine-tuned models substantially outperform from-scratch and zero-shot approaches for kinetic properties but show partial loss of long-range physics.
  arXiv  Detail & Related papers  (2025-08-27T13:24:41Z)
• BioGraphFusion: Graph Knowledge Embedding for Biological Completion and Reasoning [0.8983181722105922]
  We introduce BioGraphFusion, a novel framework for deeply synergistic semantic and structural learning.<n> Experiments across three key biomedical tasks demonstrate BioGraphFusion's superior performance over state-of-the-art KE, GNN, and ensemble models.
  arXiv  Detail & Related papers  (2025-07-19T04:03:42Z)
• VitaGraph: Building a Knowledge Graph for Biologically Relevant Learning Tasks [8.962235896860294]
  We present a comprehensive biological knowledge graph constructed by integrating and refining multiple publicly available datasets.<n>The resulting resource represents a coherent and reliable biological knowledge graph that serves as a state-of-the-art platform to advance research in computational biology and precision medicine.
  arXiv  Detail & Related papers  (2025-05-16T12:43:04Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• Differentiable Agent-based Epidemiology [71.81552021144589]
  We introduce GradABM: a scalable, differentiable design for agent-based modeling that is amenable to gradient-based learning with automatic differentiation. GradABM can quickly simulate million-size populations in few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-07-20T07:32:02Z)
• Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study [62.376800537374024]
  We study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance.
  arXiv  Detail & Related papers  (2021-06-17T17:55:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.