  While deep learning has seen many recent applications to drug discovery, most
have focused on predicting activity or toxicity directly from chemical
structure. Phenotypic changes exhibited in cellular images are also indications
of the mechanism of action (MoA) of chemical compounds. In this paper, we show
how pre-trained convolutional image features can be used to assist scientists
in discovering interesting chemical clusters for further investigation. Our
method reduces the dimensionality of raw fluorescent stained images from a high
throughput imaging (HTI) screen, producing an embedding space that groups
together images with similar cellular phenotypes. Running standard unsupervised
clustering on this embedding space yields a set of distinct phenotypic
clusters. This allows scientists to further select and focus on interesting
clusters for downstream analyses. We validate the consistency of our embedding
space qualitatively with t-sne visualizations, and quantitatively by measuring
embedding variance among images that are known to be similar. Results suggested
the usefulness of our proposed workflow using deep learning and clustering and
it can lead to robust HTI screening and compound triage.
