PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks
- José JiménezJosé JiménezComputational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88, 08003, Barcelona, SpainMore by José Jiménez
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- Davide SabbadinDavide SabbadinComputational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88, 08003, Barcelona, SpainMore by Davide Sabbadin
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- Alberto CuzzolinAlberto CuzzolinAcellera, Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88, 08003, Barcelona, SpainMore by Alberto Cuzzolin
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- Gerard Martínez-RosellGerard Martínez-RosellAcellera, Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88, 08003, Barcelona, SpainMore by Gerard Martínez-Rosell
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- Jacob GoraJacob GoraGlobal Discovery Chemistry, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United StatesDepartment of Mathematics and Computer Science, Freie Universität Berlin, Takustr. 9, 14195 Berlin, GermanyMore by Jacob Gora
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- John ManchesterJohn ManchesterGlobal Discovery Chemistry, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United StatesMore by John Manchester
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- José DucaJosé DucaGlobal Discovery Chemistry, Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United StatesMore by José Duca
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- Gianni De Fabritiis*Gianni De Fabritiis*E-mail: [email protected]Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88, 08003, Barcelona, SpainAcellera, Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88, 08003, Barcelona, SpainInstitució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, SpainMore by Gianni De Fabritiis
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.
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