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

TLDR

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. The study demonstrates that deep neural networks trained on large transcriptional response datasets can classify drugs into therapeutic categories based solely on their transcriptional profiles and proposes using confusion matrices for drug repositioning. The authors trained a DNN on 678 drug perturbation samples from the LINCS Project across A549, MCF‑7, and PC‑3 cell lines, linking gene‑level and pathway‑activation‑scored transcriptomic data at 6‑ and 24‑hour time points to 12 MeSH therapeutic categories. The DNN achieved high classification accuracy, outperforming SVM on all multiclass problems, with pathway‑level models performing significantly better, thereby proving that deep learning can recognize pharmacological properties across biological systems and conditions.

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.