Conditional Monge Gap enables generalizable single-cell perturbation modelling
5 affiliations
- 1Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- 2IBM Research Europe, Zurich, Switzerland
- 3Biomedical Data Science Center, Lausanne University Hospital, Lausanne, Switzerland
- 4Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- 5Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
Abstract
Learning the response of single cells to various treatments offers great potential to enable targeted therapies. In this context, neural optimal transport has emerged as a principled methodological framework because it inherently accommodates the challenges of unpaired data induced by cell destruction during data acquisition. However, most existing optimal transport approaches are incapable of conditioning on different treatment contexts (for example, time, drug treatment, drug dose or cell type), and we still lack methods that unanimously show promising generalizability to unseen treatments. Here we propose the Conditional Monge Gap (CMonge), which learns optimal transport maps conditionally on arbitrary covariates. We demonstrate its value in predicting single-cell perturbation responses conditional to one or more drugs, drug dose or combinations thereof. We found that our conditional models achieve results comparable with and sometimes even superior to the condition-specific state-of-the-art single-cell RNA sequencing as well as multiplexed protein imaging data. Notably, by scaling to hundreds of conditions and training on hundreds of millions of drugs, we enable cross-task learning and unlock generalizability to unseen drugs. Our method widely outperforms other conditional models in capturing heterogeneity in cell populations. In short, CMonge is mathematically grounded, highly parameter-efficient relative to single-cell foundation models and yields accurate predictions for unseen drugs using only the compound structure. Thus, it opens a practical route for accelerating drug discovery and repurposing. Driessen et al. present a conditional optimal transport method that can model the distribution shift between perturbed and unperturbed cell transcriptomes and that can generalize to unseen contexts.
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BibTeX
@article{driessen2026conditional,
title = {{Conditional Monge Gap enables generalizable single-cell perturbation modelling}},
author = {Alice Driessen and Dhruva Abhijit Rajwade and Benedek Harsanyi and Marianna Rapsomaniki and Jannis Born},
journal = {Nature Machine Intelligence},
year = {2026},
month = jun,
doi = {10.1038/s42256-026-01242-8},
url = {https://doi.org/10.1038/s42256-026-01242-8},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Conditional Monge Gap enables generalizable single-cell perturbation modelling
AU - Alice Driessen
AU - Dhruva Abhijit Rajwade
AU - Benedek Harsanyi
AU - Marianna Rapsomaniki
AU - Jannis Born
PY - 2026
DA - 2026/06/01
JO - Nature Machine Intelligence
DO - 10.1038/s42256-026-01242-8
UR - https://doi.org/10.1038/s42256-026-01242-8
AB - Learning the response of single cells to various treatments offers great potential to enable targeted therapies. In this context, neural optimal transport has emerged as a principled methodological framework because it inherently accommodates the challenges of unpaired data induced by cell destruction during data acquisition. However, most existing optimal transport approaches are incapable of conditioning on different treatment contexts (for example, time, drug treatment, drug dose or cell type), and we still lack methods that unanimously show promising generalizability to unseen treatments. Here we propose the Conditional Monge Gap (CMonge), which learns optimal transport maps conditionally on arbitrary covariates. We demonstrate its value in predicting single-cell perturbation responses conditional to one or more drugs, drug dose or combinations thereof. We found that our conditional models achieve results comparable with and sometimes even superior to the condition-specific state-of-the-art single-cell RNA sequencing as well as multiplexed protein imaging data. Notably, by scaling to hundreds of conditions and training on hundreds of millions of drugs, we enable cross-task learning and unlock generalizability to unseen drugs. Our method widely outperforms other conditional models in capturing heterogeneity in cell populations. In short, CMonge is mathematically grounded, highly parameter-efficient relative to single-cell foundation models and yields accurate predictions for unseen drugs using only the compound structure. Thus, it opens a practical route for accelerating drug discovery and repurposing. Driessen et al. present a conditional optimal transport method that can model the distribution shift between perturbed and unperturbed cell transcriptomes and that can generalize to unseen contexts.
SN - 2522-5839
ER -