QuNetMed: Quantum Algorithms for Network Medicine

Project funded by Fundação para a Ciência e a Tecnologia, I.P. (FCT) (Concurso de Projetos de I&D em Todos os Domínios Científicos - 2022 - PEX; Concurso de Projetos de I&D em Todos os Domínios Científicos - 2022), grant 2022.05558.PTDC. Funded amount: EUR 49,729.40, as stated by the source. Runs 2023-01-01 to 2024-12-31. Keywords: quantum algorithms, complex network, biological networks, Exact sciences. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2026-07-23); national funding data, not CORDIS. Abstract given by the source: The goal of this project is to research the potential of using quantum algorithms to solve complex network problems, with a focus on biological networks. The term “biological complex networks” is broad, encompassing several biological processes that can be described as a set of interacting nodes, ranging from protein–protein interaction networks to food webs [1-4]. The idea of using quantum computation to study complex networks was explored in [5] for general networks and in [6] for biological ones. In [5] the authors show that quantum computation can be used to find communities in signed graphs [3], and in [6] that quantum computation can be used to predict missing links in protein-protein interaction networks. The question we want to address with this project is what other problems in biological networks and network medicine can be tackled with quantum computation. The first problem we want to address is finding effective drug combinations for specific diseases using networks. Finding effective drug combinations for anticancer therapies using a network approach often requires a complex analysis that combines data from protein-protein interaction networks, drug-protein interactions, and disease modules [7]. There are more than 1000 drugs approved by the FDA [7], and approximately 3000 human diseases. As such, testing all pairwise or even tree-way combinations of drugs for each disease is a computationally daunting task. The question becomes even harder if a patient has several diseases: how can we find the best combinations to treat each disease, but that do not negatively interact with each other? The ever-growing complexity of this problem makes it a promising candidate to be addressed using quantum computation. The second problem we are going to look at is predicting unknown cancer driver genes using network data, which has attracted a lot of attention lately. Most notably, in 2018, a novel approach to identify cancer driver genes based on network data was [shortened; the full text is on the OpenAIRE page]

Original source.

Source: OpenAIRE Graph: projects of national funders, OpenAIRE, from the project data of Fundação para a Ciência e a Tecnologia, I.P. (FCT). Retrieved 2026-10-22. Licence: Creative Commons Attribution 4.0 (OpenAIRE Graph). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: Creative Commons Attribution 4.0 (CC BY 4.0).

AI-assisted summary: QuNetMed is a project funded by FCT with a grant of EUR 49,729.40, running from January 1, 2023, to December 31, 2024. It aims to research quantum algorithms for solving complex network problems, particularly in biological networks. Suggested technology pillar: computing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.