Learning Drug Functions from Chemical Structures with Convolutional Neural Networks and Random Forests

Empirical testing of chemicals for
drug efficacy costs many billions
of dollars every year. The ability to predict the action of molecules
in silico would greatly increase the speed and decrease the cost of
prioritizing drug leads. Here, we asked whether drug function, defined
as MeSH “therapeutic use” classes, can be predicted
from only a chemical structure. We evaluated two chemical-structure-derived
drug classification methods, chemical images with convolutional neural
networks and molecular fingerprints with random forests, both of which
outperformed previous predictions that used drug-induced transcriptomic
changes as chemical representations. This suggests that the structure
of a chemical contains at least as much information about its therapeutic
use as the transcriptional cellular response to that chemical. Furthermore,
because training data based on chemical structure is not limited to
a small set of molecules for which transcriptomic measurements are
available, our strategy can leverage more training data to significantly
improve predictive accuracy to 83–88%. Finally, we explore
use of these models for prediction of side effects and drug-repurposing
opportunities and demonstrate the effectiveness of this modeling strategy
for multilabel classification.