RePert: Effect-Grounded Identity Learning for Generalizable Inverse Perturbation Retrieval

Inverse perturbation retrieval seeks to identify the intervention responsible for an observed cellular response, yet current retrievers generalize poorly beyond the cellular settings seen during training. Two barriers underlie this failure: (i) response distributions for the same perturbation can differ substantially across cell lines, so patterns learned in one cell line may not transfer to another. (ii) perturbation identity embeddings learned from training cell lines lack direct functional evidence and transfer poorly to unseen ones. To address these barriers, we introduce RePert, a novel Response-to-Perturbation framework with functionally-grounded two-stage retrieval. RePert introduces two key innovations: (i) Effect encoder pretraining learns transferable effect representations from multiple responses sharing the same perturbation or target. (ii) Effect-grounded identity retrieval builds a non-parametric effect memory and uses its prototypes to ground identity embeddings. RePert combines identity and effect matching views through a reliability-calibrated score. We evaluate RePert in six genetic and chemical retrieval settings spanning in-domain evaluation, held-out cell-line generalization, and zero-shot cross-platform transfer to external single-cell platforms. For gene-level retrieval, RePert improves recall@10 over strongest baseline by factors of $4.41\times$, $3.10\times$, and $1.84\times$ in single-gene, multiple-gene, and held-out cell-line retrieval, respectively. For compound-level cross-platform transfer without target-platform training, it improves top-1% macro recall against strongest baseline by factors of $1.17\times$, $1.52\times$, and $1.15\times$ on Cellarity-Cancer, Cellarity-Primary, and sciPlex3, respectively. These results establish transferable effect learning and identity grounding as a robust and promising design for generalizable inverse perturbation retrieval. Code is available at https://anonymous.4open.science/r/RePert-DEC3/.