DACL: Predicting Responses to Pairs of Unseen Genes by Reusing Single-Perturbation Supervision

Combinatorial CRISPR screens measure a small fraction of possible gene pairs, so models must predict responses to gene pairs never perturbed in training. In this strict both-unseen setting, the only information about the test genes is a response-free descriptor such as Gene Ontology annotations. Learned structure helps only if these descriptors can retrieve it. We introduce Deployment-Addressable Constituent Learning (DACL), which learns one map from a gene's descriptor to its perturbation effect. Single perturbations supervise the map directly, while pairs supervise the sum of two predicted effects. An unseen pair is predicted by that sum. On the three official Norman Perturb-seq splits, DACL reaches a Pearson-$\Delta$ of 0.401 on pairs of unseen genes, against 0.337 for biolord and 0.191 for GEARS. Over 70 further splits it outperforms biolord and direct pair models given the same Gene Ontology descriptors, and the advantage stays positive, though unresolved at the gene level, with language-model gene embeddings. On a genetic-interaction screen, matched controls show that single perturbations help through the additive path, not a larger function class. Direct pair models remain preferable on some screens with strong pair-specific structure. In two prospective evaluations, training data alone always selected the direct model, which had lower normalized pair error in all 45 panels.