MORGaN: self-supervised multi-relational graph learning for drug target discovery

Martina Occhetta, Anniek Myatt, Manikhandan A. V. Mudaliar, Conrad Bessant

Additional Submission Instructions: For the camera-ready version, please include the author names and affiliations, funding disclosures, and acknowledgements.

Track: Track 1: Original Research/Position/Education/Attention Track

Keywords: network biology, graph learning, target identification

TL;DR: We introduce MORGaN, a self-supervised graph learning framework integrating multi-omic features and multi-relational gene networks, significantly outperforming existing methods in druggable cancer gene prediction.

Abstract: Identifying therapeutically tractable targets remains difficult, partly because disease biology is distributed across multiple molecular layers and relation types, while labeled data are scarce. We present MORGaN, a self-supervised framework for node classification on multi-omic, multi-relation gene networks that learns structure-aware embeddings and outputs calibrated scores to prioritize therapeutic targets. On a pan-cancer graph integrating TCGA multi-omics and diverse biological relation types, MORGaN outperforms state-of-the-art biological node classification models across metrics (AUPR: $0.815 \rightarrow 0.888$; $+9$\%). Ablation studies highlight that both relation diversity and the in-layer fusion architecture are necessary for these gains. Prioritized targets are biologically coherent: high-confidence hits are enriched for pharmaceutically tractable families and ligand–receptor signaling cascades. Post hoc explainability analyses recover compact, pathway-consistent motifs around both known and putative novel targets, and concordance with external resources further supports plausibility. MORGaN thus delivers label-efficient, interpretable node classification for target discovery and can be readily adapted to other diseases, species, and node classification tasks. Source code and documentation are available at https://github.com/martina-occhetta/MORGaN.

Submission Number: 96