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

Abstract

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.

Keywords: DNN; confusion matrix; deep learning; deep neural networks; drug discovery; drug repurposing; predictor.

Figure 1

Study design. Gene expression data from LINCS Project was linked to 12 MeSH therapeutic use categories. DNN was trained separately on gene expression level data for “landmark genes” and pathway activation scores for significantly perturbed samples, forming an input layers of 977 and 271 neural nodes, respectively.

Figure 2

Classification results. Classification performance of DNN and SVM trained on signaling pathways (a, b, c) and landmark genes (d, e, f) for 3, 5 and 12 drug classes, respectively, after 10 fold cross validation. Training and validation set results are shown in gray and green colors, respectively.

Figure 3

Validation confusion matrix representing deep neural network classification performance over a set of drugs profiled for A549, MCF7 and PC3 cell lines, belonging to 3 (a), 5 (b) and 12 (c) therapeutic classes. C(i,j) element is a sample count of how many times i was the truth and j was predicted.