Drug-disease Networks and Drug Repurposing
Austin Polanco, Mark E. J. Newman
Abstract
Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we present a novel network of drugs and the diseases they treat, compiled using a combination of existing textual and machine-readable databases, natural-language processing tools, and hand curation, and analyze it using network-based link prediction methods to identify potential drug-disease combinations.
Introduction
Drug repurposing, the practice of finding new uses for established medications, is a vital part of the pharmaceutical development landscape. A fundamental part of the repurposing process is the identification of promising candidate drugs, and a significant amount of effort has been invested in the development of computational and statistical methods for performing this task. In this paper, we approach the problem using tools from the burgeoning field of network science, viewing it as a link prediction problem on a network of drugs and the conditions they treat.
Materials and Methods:
As discussed in the introduction, our network is based solely on known therapeutic drug-disease associations. The starting point is the DrugBank database (version 5.1.10, circa 2024), an online index of over 15 000 drugs, with targets, chemical data, prescribing information, and other details. Many of these drugs are experimental or of dubious therapeutic value and we remove a significant number from the set, including drugs not approved for clinical use, drugs labeled as supplements, cosmetics, food or food additives, household products, allergens, or contrast agents, and drugs belonging to no known category.
Discussion
In this paper we have described the construction of a data set of 2620 drugs and 1669 diseases and conditions for which they are indicated, based on several pre-existing, publicly available databases, analyzed using a combination of machine learning methods and human data curation. The resulting data set describes 8946 known drug-disease interactions.
Citation: Polanco A, Newman MEJ (2025) Drug-disease networks and drug repurposing. PLoS Comput Biol 21(10): e1013595. https://doi.org/10.1371/journal.pcbi.1013595
Editor: Yamir Moreno, University of Zaragoza: Universidad de Zaragoza, SPAIN
Received: May 8, 2025; Accepted: October 6, 2025; Published: October 16, 2025.
Copyright: © 2025 Polanco, Newman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data and code for the work described here is available at https://github.com/apolanco115/drug-dis-lp, with the exception of previously available data and code by other authors, which can be found at the references and locations cited in the text.
Funding: This work was funded in part by the US National Science Foundation under grant numbers DMS–2005899 and DMS–2404617 (to MEJN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
