PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks
José Jiménez-Luna, Davide Sabbadin, Alberto Cuzzolin, Gérard Martinez, Jacob Gora, John I. Manchester, José S. Duca, Gianni De Fabritiis · Journal of Chemical Information and Modeling · 2018
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 .