# scDFM: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction

arXiv: https://arxiv.org/abs/2602.07103 · HTML: https://mlsanity.com/paper/2602.07103/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 6 of 6)

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View](https://mlsanity.com/paper/2403.15500/) (2024)
- [Diversity by Design: Addressing Mode Collapse Improves scRNA-seq Perturbation Modeling on Well-Calibrated Metrics](https://mlsanity.com/paper/2506.22641/) (2025)
- [Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation](https://mlsanity.com/paper/2506.21107/) (2025)
- [Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations](https://mlsanity.com/paper/2507.02980/) (2025)

## Built on by (most cited first, 11 of 11)

- [Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells](https://mlsanity.com/paper/2603.25240/) (2026)
- [OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction](https://mlsanity.com/paper/2606.12838/) (2026)
- [D2R2: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction](https://mlsanity.com/paper/2608.15288/) (2026)
- [PerturbCellRL: Aligning Distributions and Grounding Biology via Post-Training Perturbation Generators](https://mlsanity.com/paper/2606.27752/) (2026)
- [PerturbMap: Cross-Context Transfer of Single-Cell Perturbation Responses](https://mlsanity.com/paper/2607.28090/) (2026)
- [Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling](https://mlsanity.com/paper/2608.06824/) (2026)
- [DeMixPert: Decomposed Response Modeling with Gaussian Mixtures for OOD Single-Cell Perturbation Prediction](https://mlsanity.com/paper/2608.23114/) (2026)
- [DRIFT: Disentangled Responsive-Invariant Flow Transport for Single-Cell Perturbation Prediction](https://mlsanity.com/paper/2609.35106/) (2026)
- [Generalizable single-cell perturbation response prediction using energy-guided flow matching](https://mlsanity.com/paper/2610.02232/) (2026)
- [TempoBridge: Source-Conditioned Flow Matching with Optimal Transport Couplings for Single-Cell Population Transitions](https://mlsanity.com/paper/2610.04945/) (2026)
- [Predicting Unseen Scientific Interactions: Conditional Flow Models Hallucinate Non-Additive Effects](https://mlsanity.com/paper/2610.20288/) (2026)

## Abstract (arXiv)

A central goal in systems biology and drug discovery is to predict the transcriptional response of cells to perturbations. This task is challenging due to the noisy and sparse nature of single-cell measurements, as well as the fact that perturbations often induce population-level shifts rather than changes in individual cells. Existing deep learning methods typically assume cell-level correspondences, limiting their ability to capture such global effects. We present scDFM, a generative framework based on conditional flow matching that models the full distribution of perturbed cells conditioned on control states. By incorporating a maximum mean discrepancy (MMD) objective, our method aligns perturbed and control populations beyond cell-level correspondences. To further improve robustness to sparsity and noise, we introduce the Perturbation-Aware Differential Transformer (PAD-Transformer), a backbone architecture that leverages gene interaction graphs and differential attention to capture context-specific expression changes. Across multiple genetic and drug perturbation benchmarks, scDFM consistently outperforms prior methods, demonstrating strong generalization in both unseen and combinatorial settings. In the combinatorial setting, it reduces mean squared error by 19.6% relative to the strongest baseline. These results highlight the importance of distribution-level generative modeling for robust in silico perturbation prediction. The code is available at https://github.com/AI4Science-WestlakeU/scDFM