Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Deep
learning is rapidly advancing many areas of science and technology
with multiple success stories in image, text, voice and video recognition,
robotics, and autonomous driving. In this paper we demonstrate how
deep neural networks (DNN) trained on large transcriptional response
data sets can classify various drugs to therapeutic categories solely
based on their transcriptional profiles. We used the perturbation
samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from
the LINCS Project and linked those to 12 therapeutic use categories
derived from MeSH. To train the DNN, we utilized both gene level transcriptomic
data and transcriptomic data processed using a pathway activation
scoring algorithm, for a pooled data set of samples perturbed with
different concentrations of the drug for 6 and 24 hours. In both pathway
and gene level classification, DNN achieved high classification accuracy
and convincingly outperformed the support vector machine (SVM) model
on every multiclass classification problem, however, models based
on pathway level data performed significantly better. For the first
time we demonstrate a deep learning neural net trained on transcriptomic
data to recognize pharmacological properties of multiple drugs across
different biological systems and conditions. We also propose using
deep neural net confusion matrices for drug repositioning. This work
is a proof of principle for applying deep learning to drug discovery
and development.