MolCLIP: A Molecular-Auxiliary CLIP Framework for Identifying Drug Mechanism of Action Based on Time-Lapsed Mitochondrial Images
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Drug Mechanism of Action (MoA) mainly investigates how drug molecules
interact with cells, which is crucial for drug discovery and clinical
application. Recently, deep learning models have been used to recognize MoA by
relying on high-content and fluorescence images of cells exposed to various
drugs. However, these methods focus on spatial characteristics while
overlooking the temporal dynamics of live cells. Time-lapse imaging is more
suitable for observing the cell response to drugs. Additionally, drug molecules
can trigger cellular dynamic variations related to specific MoA. This indicates
that the drug molecule modality may complement the image counterpart. This
paper proposes MolCLIP, the first visual language model to combine microscopic
cell video- and molecule-modalities. MolCLIP designs a molecule-auxiliary CLIP
framework to guide video features in learning the distribution of the molecular
latent space. Furthermore, we integrate a metric learning strategy with MolCLIP
to optimize the aggregation of video features. Experimental results on the
MitoDataset demonstrate that MolCLIP achieves improvements of 51.2% and 20.5%
in mAP for drug identification and MoA recognition, respectively.
