One Drug, MoRe Views: Response-Directed Multi-Modal Conditioning for Unseen-Drug Perturbation

Predicting transcriptional responses to unseen drugs is central to prioritising compounds for experimental profiling, yet existing predictors generalise poorly to held-out drugs. We attribute this to two coupled limitations: drugs are represented by chemical structure alone, which only partly determines their mechanism, and models are trained to reconstruct whole expression profiles, an objective dominated by background variation rather than the perturbation effect. We introduce MoRe, a model-agnostic plug-in that addresses both. A lightweight module fuses chemical and target-informed representations into a single drug latent, and an auxiliary objective supervises the predicted mean response of each condition. Both components are trained jointly with the backbone. On an autoencoder (ChemCPA) and a diffusion model (PerturbDiff), evaluated on held-out drugs in sci-Plex3 and Tahoe-100M, MoRe improves the correlation between predicted and observed responses on the top 50 differentially expressed genes by up to 113%. Analysis shows that the drug views are complementary: they lead on different gene programmes, and fusion places each drug near training drugs with similar responses, whereas chemistry alone does not. By describing drugs by what they do as well as what they are, MoRe points to a promising direction for virtual-cell models of in silico drug perturbation.