  Image-based profiling techniques have become increasingly popular over the
past decade for their applications in target identification,
mechanism-of-action inference, and assay development. These techniques have
generated large datasets of cellular morphologies, which are typically used to
investigate the effects of small molecule perturbagens. In this work, we extend
the impact of such dataset to improving quantitative structure-activity
relationship (QSAR) models by introducing Molecule-Morphology Contrastive
Pretraining (MoCoP), a framework for learning multi-modal representation of
molecular graphs and cellular morphologies. We scale MoCoP to approximately
100K molecules and 600K morphological profiles using data from the JUMP-CP
Consortium and show that MoCoP consistently improves performances of graph
neural networks (GNNs) on molecular property prediction tasks in ChEMBL20
across all dataset sizes. The pretrained GNNs are also evaluated on internal
GSK pharmacokinetic data and show an average improvement of 2.6% and 6.3% in
AUPRC for full and low data regimes, respectively. Our findings suggest that
integrating cellular morphologies with molecular graphs using MoCoP can
significantly improve the performance of QSAR models, ultimately expanding the
deep learning toolbox available for QSAR applications.
