CellMINT: Disentangled Multimodal Learning with Biologically Informed Task Interaction for Assay Prediction in Drug Discovery

Cell Painting phenotypes are increasingly integrated with chemical perturbation information to support diverse assay prediction tasks in drug discovery. However, existing multimodal approaches often emphasize modality-shared information while underutilizing task-relevant modality-specific signals. Moreover, most multi-task methods for joint assay prediction do not explicitly model interactions among biologically related assay endpoints, limiting effective knowledge sharing across them. In this paper, we propose CellMINT, a multimodal multi-task framework that enhances Cell Painting-based prediction through disentangled multimodal learning and biologically informed task interaction. To identify task-relevant multimodal evidence, CellMINT disentangles multimodal features into modality-shared and modality-specific components, enabling each assay endpoint to adaptively aggregate the information most relevant to its prediction. To further exploit complementary information across related tasks, CellMINT constructs a biological prior from assay context to guide selective task interaction among informative endpoints. Experiments on three public Cell Painting benchmarks demonstrate that CellMINT improves average predictive performance and high-performing task coverage over unimodal and multimodal baselines, highlighting its potential for comprehensive assay profiling in drug discovery.