Drug Side Effect Prediction with Deep Learning Molecular Embedding in a Graph-of-Graphs Domain

Niccolò Pancino (), Yohann Perron, Pietro Bongini () and Franco Scarselli
Additional contact information
Niccolò Pancino: Department of Information Engineering and Mathematics (DIISM), University of Siena, 53100 Siena, Italy
Yohann Perron: Ecole Polytechnique, Institut Polytechnique de Paris, Rte de Saclay, 91120 Palaiseau, France
Pietro Bongini: Department of Information Engineering and Mathematics (DIISM), University of Siena, 53100 Siena, Italy
Franco Scarselli: Department of Information Engineering and Mathematics (DIISM), University of Siena, 53100 Siena, Italy

Mathematics, 2022, vol. 10, issue 23, 1-15

Abstract: Drug side effects (DSEs), or adverse drug reactions (ADRs), constitute an important health risk, given the approximately 197,000 annual DSE deaths in Europe alone. Therefore, during the drug development process, DSE detection is of utmost importance, and the occurrence of ADRs prevents many candidate molecules from going through clinical trials. Thus, early prediction of DSEs has the potential to massively reduce drug development times and costs. In this work, data are represented in a non-euclidean manner, in the form of a graph-of-graphs domain. In such a domain, structures of molecule are represented by molecular graphs, each of which becomes a node in the higher-level graph. In the latter, nodes stand for drugs and genes, and arcs represent their relationships. This relational nature represents an important novelty for the DSE prediction task, and it is directly used during the prediction. For this purpose, the MolecularGNN model is proposed. This new classifier is based on graph neural networks, a connectionist model capable of processing data in the form of graphs. The approach represents an improvement over a previous method, called DruGNN, as it is also capable of extracting information from the graph-based molecular structures, producing a task-based neural fingerprint (NF) of the molecule which is adapted to the specific task. The architecture has been compared with other GNN models in terms of performance, showing that the proposed approach is very promising.

Keywords: graph neural networks; drug side effect prediction; node classification; deep learning; transductive learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/23/4550/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/23/4550/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:23:p:4550-:d:990381

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().