Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses
Year: 2025
Type: conference-paper
Abstract: Phenotypic drug discovery has attracted widespread attention because of its potential to identify bioactive molecules. Transcriptomic profiling provides a comprehensive reflection of phenotypic changes in cellular responses to external perturbations. In this paper, we propose XTransferCDR, a novel generative framework designed for feature decoupling and transferable representation learning across domains. Given a pair of perturbed expression profiles, our approach decouples the perturbation representations from basal states through domain separation encoders and then cross-transfers them in the latent space. The transferred representations are then used to reconstruct the corresponding perturbed expression profiles via a shared decoder. This cross-transfer constraint effectively promotes the learning of transferable drug perturbation representations. We conducted extensive evaluations of our model on multiple datasets, including single-cell transcriptional responses to drugs and single- and combinatorial genetic perturbations. The experimental results show that XTransferCDR achieved better performance than current state-of-the-art methods, showcasing its potential to advance phenotypic drug discovery.
Authors: Hui Liu, Shikai Jin
Institution: Nanjing Tech University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
Awards: G3638561661, G5232981075
OpenAlex ID: W4409364000