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Mol Pharm. 2016 Jul 5;13(7):2524-30. doi: 10.1021/acs.molpharmaceut.6b00248. Epub 2016 Jun 8.

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

Author information

  • 1Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States.
  • 2Datalytic Solutions , 1101 Yale Boulevard NE, Albuquerque, New Mexico 87106, United States.
  • 3The Mind Research Network , Albuquerque, New Mexico 87106, United States.
  • 4The Biogerontology Research Foundation , Oxford, U.K.

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

PMID:
27200455
PMCID:
PMC4965264
DOI:
10.1021/acs.molpharmaceut.6b00248
[PubMed - in process]
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