An immune world model for multiscale forecasting and therapeutic hypothesis generation

2026-09-13

AI Summary

该研究使用进化AI构建了免疫世界模型，以解决多尺度预测和治疗假设生成问题，通过整合细胞、组织和个体层面信息，提高了干预预测准确性。

Abstract

Immune therapies act across cell-intrinsic programs, tissue ecosystems, and patient-specific immune states, yet most predictors address these scales separately. We used a governed evolutionary AI Scientist to construct the Immune World Model, an action-conditioned model that learns how interventions move immune states across cellular, tissue, and individual levels. The Immune World Model--building Scientist searched candidate architectures and workflows, and the resulting world model was frozen before independent confirmation. The frozen model generalized to unseen interventions and biological contexts, recovered intervention-specific cellular programs, integrated cell and tissue information to improve ecosystem and patient-response prediction, and forecast unseen perturbation combinations. Immune World Model--guided analysis then combined measured perturbations with cross-axis inference to nominate IL-36$γ$ plus SIRP$α$ inhibition as a complementary-axis therapeutic hypothesis, whereas a governed self-correction audit rejected every screened cytokine pair. The Immune World Model provides a framework for multiscale immune simulation that connects AI Scientist-driven model construction, intervention forecasting, and the generation of prospectively testable therapeutic hypotheses.

Problem

• immune therapies
• multiscale forecasting
• immune states

Innovation

• multiscale forecasting
• immune world model
• AI Scientist
• therapeutic hypothesis generation
• cross-axis inference