Artificial intelligence virtual cell immune recovery model for screening traditional Chinese medicine ingredients

Abstract

Screening therapeutic candidates from single-cell transcriptomes requires a target that is closer to treatment response than disease-signature reversal. In immune diseases, post-treatment recovery may follow patient- and lineage-specific trajectories rather than a simple return along the pretreatment disease axis. We developed ImmuneNavi, an artificial intelligence virtual cell (AIVC) immune recovery model for ranking traditional Chinese medicine ingredients from paired PBMC data. The model maps heterogeneous PBMC cohorts to a common healthy immune coordinate system, constructs patient-lineage disease and recovery states, and processes ITCM treated-control profiles into a fixed ingredient perturbation bank. Patient and ingredient states are represented in matched gene, pathway and transcription-factor views, allowing the model to combine local transcriptional direction with more stable program-level features. A matcher trained on one paired treatment cohort preserved recovery-aligned ingredient rankings in independent PBMC cohorts without redefining the feature space, candidate set or preprocessing procedure. ImmuneNavi provides an AIVC model that uses paired immune-state measurements to screen natural-product candidates for experimental follow-up.