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Phenotype-based screening has attracted much attention for identifyingcell-active compounds. Transcriptional and proteomic profiles of cellpopulation or single cells are informative phenotypic measures of cellularresponses to perturbations. In this paper, we proposed a deep learningframework based on encoder-decoder architecture that maps the initial cellularstates to a latent space, in which we assume the effects of drug perturbationon cellular states follow linear additivity. Next, we introduced the cycleconsistency constraints to enforce that initial cellular state subjected todrug perturbations would produce the perturbed cellular responses, and,conversely, removal of drug perturbation from the perturbed cellular stateswould restore the initial cellular states. The cycle consistency constraintsand linear modeling in latent space enable to learn interpretable andtransferable drug perturbation representations, so that our model can predictcellular response to unseen drugs. We validated our model on three differenttypes of datasets, including bulk transcriptional responses, bulk proteomicresponses, and single-cell transcriptional responses to drug perturbations. Theexperimental results show that our model achieves better performance thanexisting state-of-the-art methods.

Interpretable Modeling of Single-cell perturbation Responses to Novel Drugs Using Cycle Consistence Learning