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PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks

Jimenez, Jose and Sabbadin, Davide and Cuzzolin, Alberto and Martinez-Rosell, Gerard and Gora, Jacob and Manchester, John and Duca, Jose and DeFabritiis, Gianni (2019) PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks. Journal of chemical information and modeling, 59 (3). pp. 1172-1181. ISSN 1549960X

Abstract

Drug discovery suffers from high attrition because compounds initially deemed as promising can later show ineffectiveness or toxicity resulting from a poor understanding of their activity profile. In this work, we describe a deep self-normalizing neural network model for the prediction of molecular pathway association and evaluate its performance, showing an AUC ranging from 0.69 to 0.91 on a set of compounds extracted from ChEMBL and from 0.81 to 0.83 on an external data set provided by Novartis. We finally discuss the applicability of the proposed model in the domain of lead discovery. A usable application is available via PlayMolecule.org .

Item Type: Article
Date Deposited: 20 Aug 2019 00:45
Last Modified: 20 Aug 2019 00:45
URI: https://oak.novartis.com/id/eprint/37908

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