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

Abstract

Motivation: 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.Objective: We present GraphPert, a Graph Autoencoder (GAE) framework that propagates transcriptional signatures through the human PPI to enable efficient network-based virtual creening. Methods: 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.Results: 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.Conclusion: 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.