Right for the Right Reasons? Testing Whether Cellular Profiles Explain Gene-Drug Retrieval

Image-based cellular profiling records how cells change after a gene is modified or a drug is applied. Comparing these responses can reveal gene–drug relationships and help prioritize candidates for drug discovery and repurposing. However, a high retrieval score does not show why a model recovered a known relationship. The model may use the cellular response, favor genes or drugs with many recorded partners, or exploit the relationship graph, in which genes and drugs are nodes and recorded relationships are edges. We introduce a controlled evaluation framework that asks two questions: does a model recover known gene–drug relationships better than randomized relationships, and do cellular features or graph connections support that recovery? The framework combines held-out retrieval with degree-preserving randomized relationships. It separates cellular features, which describe observed cell responses, from graph topology, the pattern of recorded gene–drug connections. We evaluate RxRx3-Core, CPJUMP1, MOTIVE-CRISPR, and a private angiogenesis dataset. CPJUMP1 supports recovery in both directions, RxRx3-Core is stronger from drugs to genes, and the angiogenesis data have no consistently dominant direction across metrics. MOTIVE-CRISPR supports nonlinear feature-based retrieval, while its graph controls expose the same structural cautions. Among the tested models, adding nonlinearity or graph message passing therefore does not consistently produce stronger or more informative alignment. The main contribution is a practical way to determine whether a result is supported by cellular information, graph structure, or both. This provides a clearer basis for selecting gene–drug candidates for experimental follow-up.