# Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses

arXiv: https://arxiv.org/abs/2412.19228 · HTML: https://mlsanity.com/paper/2412.19228/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 33 references that are in the map.
- First posted on arXiv 26 Dec 2024.

## Built on by (most cited first, 3 of 3)

- [STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations](https://mlsanity.com/paper/2602.10156/) (2026)
- [Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap](https://mlsanity.com/paper/2504.08328/) (2025)
- [Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses](https://mlsanity.com/paper/2606.30695/) (2026)

## Abstract (arXiv)

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.