Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Frederik G. Hansson, Niklas Gesmar Madsen, Lea G. Hansen, Tadas Jakočiūnas, Bettina Lengger, Jay D. Keasling, Michael K. Jensen, Carlos G. Acevedo-Rocha () and Emil D. Jensen ()
Additional contact information
Frederik G. Hansson: Technical University of Denmark
Niklas Gesmar Madsen: Technical University of Denmark
Lea G. Hansen: Biomia Aps Lersø Parkallé 44
Tadas Jakočiūnas: Technical University of Denmark
Bettina Lengger: Technical University of Denmark
Jay D. Keasling: Joint BioEnergy Institute
Michael K. Jensen: Technical University of Denmark
Carlos G. Acevedo-Rocha: Technical University of Denmark
Emil D. Jensen: Technical University of Denmark

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-59418-6 Abstract (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:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59418-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-59418-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().