title: 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.
authors.affiliation: Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States.
authors.forename: Alexander
authors.initials: A
authors.lastname: Aliper
authors.affiliation: Datalytic Solutions , 1101 Yale Boulevard NE, Albuquerque, New Mexico 87106, United States.|The Mind Research Network , Albuquerque, New Mexico 87106, United States.
authors.forename: Sergey
authors.initials: S
authors.lastname: Plis
authors.affiliation: Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States.
authors.forename: Artem
authors.initials: A
authors.lastname: Artemov
authors.affiliation: The Mind Research Network , Albuquerque, New Mexico 87106, United States.
authors.forename: Alvaro
authors.initials: A
authors.lastname: Ulloa
authors.affiliation: Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States.
authors.forename: Polina
authors.initials: P
authors.lastname: Mamoshina
authors.affiliation: Insilico Medicine, ETC, B301, Johns Hopkins University , Baltimore, Maryland 21218, United States.|The Biogerontology Research Foundation , Oxford, U.K.
authors.forename: Alex
authors.initials: A
authors.lastname: Zhavoronkov
mesh_terms: D000072283:A549 Cells; D000465:Algorithms; D055808:Drug Discovery; D058492:Drug Repositioning / Q000379:methods*; D006801:Humans; D061986:MCF-7 Cells; D016571:Neural Networks, Computer; D060388:Support Vector Machine; D059467:Transcriptome / Q000235:genetics
keywords: DNN; confusion matrix; deep learning; deep neural networks; drug discovery; drug repurposing; predictor