  Modeling the relationship between chemical structure and molecular activity
is a key goal in drug development. Many benchmark tasks have been proposed for
molecular property prediction, but these tasks are generally aimed at specific,
isolated biomedical properties. In this work, we propose a new cross-modal
small molecule retrieval task, designed to force a model to learn to associate
the structure of a small molecule with the transcriptional change it induces.
We develop this task formally as multi-view alignment problem, and present a
coordinated deep learning approach that jointly optimizes representations of
both chemical structure and perturbational gene expression profiles. We
benchmark our results against oracle models and principled baselines, and find
that cell line variability markedly influences performance in this domain. Our
work establishes the feasibility of this new task, elucidates the limitations
of current data and systems, and may serve to catalyze future research in small
molecule representation learning.
