Complex graph neural networks for medication interaction verification
Gustavo Westarb, Stéfano Frizzo Stefenon, Aurélio Faustino Hoppe, Andreza Sartori, Anne Carolina Rodrigues Klaar, Valderi Reis Quietinho Leithardt
Abstract
This paper presents the development and application of graph neural networks to verify drug interactions, consisting of drug-protein networks. For this, the DrugBank databases were used, creating four complex networks of interactions: target proteins, transport proteins, carrier proteins, and enzymes. The Louvain and Girvan-Newman community detection algorithms were used to establish communities and validate the interactions between them. Positive results were obtained when checking the interactions of two sets of drugs for disease treatments: diabetes and anxiety; diabetes and antibiotics. There were found 371 interactions by the Girvan-Newman algorithm and 58 interactions via Louvain.