发表机构
IBENS; Ecole Normale Supérieure; Université PSL; Institut Curie; INSERM; Mines ParisTech; Iktos（法国生物学与医学研究所; 巴黎高等师范学院; 巴黎文理大学; 居里研究所; 法国国家健康与医学研究院; 巴黎高科矿业学院; Iktos公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出分子条件神经最优传输（NOT）模型，以分子结构和阴性对照表型为输入预测细胞表型，在未见过的活性分子上性能优于基线，为相关预测提供了有潜力的框架。
AI 中文摘要
高内涵显微镜技术可实现对细胞响应化学扰动的系统性分析，但化学空间的规模使得穷尽式表型表征在实验层面难以实现，这催生了能够在无需获取对应处理细胞的情况下预测图像衍生表型的计算模型。我们将分子诱导表型预测问题建模为图像表示空间中的归纳条件传输问题：给定阴性对照表型和分子结构，目标是预测对应分子诱导的表型。我们首先评估了经典最优传输基线方法，结果显示静态耦合在大规模表型图像数据集上无法生成有用的预测结果。随后我们提出了一种分子条件神经最优传输（NOT）模型，其采用Monge-Gap正则化训练目标，以分子结构为条件信息，学习将阴性对照的未受扰动表型传输至受扰动表型。NOT模型可恢复分子特异性的表型效应，同时减少显微镜相关的技术变异，从而便于跨实验批次进行比较。在未见过的活性分子上，该模型的性能优于基线方法，证明了化学条件传输可泛化至训练期间未观测到的分子。我们还发现分子编码器是该泛化能力的主要限制因素，而在压缩表示空间中进行传输可提升性能和可扩展性。这些结果确立了NOT作为一种从分子结构和阴性对照表型预测细胞表型的有潜力框架，同时强调开发更具信息性的分子表示是提升分布外性能的关键方向。
英文摘要
High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterization experimentally infeasible. This motivates computational models that can predict image-derived phenotypes without acquiring the corresponding treated cells. We formulate molecule-induced phenotype prediction as an inductive conditional transport problem in image representation space. Given a negative-control phenotype and the structure of a molecule, we aim to predict the phenotype induced by the corresponding molecule. We first evaluate classical optimal transport baselines and show that static couplings do not yield useful predictions on large-scale phenotypic image datasets. We then introduce a molecule-conditioned Neural Optimal Transport (NOT) model with a Monge-Gap regularization training objective that learns to transport negative-control unperturbed phenotypes toward perturbed phenotypes using molecular structure as conditioning information. NOT recovers molecule-specific phenotypic effects while reducing microscopy-associated technical variation, thereby facilitating comparisons across experimental batches. On unseen active molecules, the model outperforms baseline approaches, demonstrating that chemically conditioned transport can generalize beyond the molecules observed during training. We identified the molecular encoder as the main limitation to this generalization, while transport in a compressed representation space improves performance and scalability. These results establish NOT as a promising framework for predicting cellular phenotypes from molecular structure and negative-control phenotypes, while highlighting the development of more informative molecular representations as a key direction for improving out-of-distribution performance.
CommentsAccepted at the BioImage Computing (BIC) Workshop, ECCV 2026
