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

2024/12/26 by Hui Liu, Shikai Jin, Liu, Hui +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Innovative Microfluidic and Catalytic Techniques Innovation #Machine Learning (cs.LG) #Single-cell and spatial transcriptomics

paper · pdf · doi:10.48550/arxiv.2412.19228

openalex publication_date 2024/12/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations
• Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap

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