GraphPert: A graph autoencoder framework for network-based drug repurposing using transcriptional signatures-A case study on melanoma.

Publication date: Sep 05, 2026

Network-based drug repurposing leverages the principle that drug effects propagate through protein-protein interaction (PPI) networks similarly to disease perturbations. Graph-based embedding methods exploit this principle by learning low-dimensional representations that capture the network neighborhood of disease and drug target proteins, enabling similarity-based prioritization of therapeutic candidates. However, existing approaches that learn fixed embeddings from a static graph cannot accommodate perturbations to the graph structure without costly recomputation or retraining, making them impractical for large-scale screening where each drug generates a distinct transcriptional perturbation. We present GraphPert, a Graph Autoencoder (GAE) framework that propagates transcriptional signatures through the human PPI to enable efficient network-based virtual screening. Transcriptional signatures from the Connectivity Map L1000 platform are encoded as symmetric edge-weight modifications in the PPI, scaling each interaction by the expression changes of both participating proteins. A GAE pre-trained on the human PPI (N=12,458 proteins) produces latent displacements for each perturbation, and compounds are ranked by cosine similarity to a therapeutically relevant reference-the BRAF V600E knockout (KO) in A375 melanoma cells. In retrospective screening against a decoy library of known MAPK cascade inhibitors seeded among compounds without known MAPK pathway activity (1:9 ratio), GraphPert achieves AUC >0. 80. Notably, while raw signature-based screening recovers predominantly compounds with targets proximal to the MAPK pathway, GraphPert additionally identifies compounds with more distal targets-including HDAC, proteasome, and PARP inhibitors-that converge on the same downstream functional modules suppressed by BRAF KO. GraphPert demonstrates that propagating transcriptional signatures through a pre-trained GAE extends the reach of virtual screening beyond direct transcriptional similarity, enabling the discovery of mechanistically diverse compounds that recapitulate the network-level effects of a therapeutic reference perturbation.

Semantics

Original Article