Chemical-Disease-Gene (CDG) association prediction-encompassing Chemical-Disease (CD), Disease-Gene (DG), and Chemical-Gene (CG) interactions-is a cornerstone of drug discovery, as it underpins target identification and drug repurposing. While these tasks are inherently synergistic, existing methods often address them in isolation, failing to capture shared heterogeneous semantics and cross-task dependencies. We hypothesize that a unified pretraining framework can learn transferable biomedical semantics across CDG tasks, with task-specific prompt tuning enabling efficient adaptive fine-tuning without full retraining. To test this hypothesis, we propose the Pretraining-Prompt-Finetuning Heterogeneous Graph Neural Network (PPF-HGNN), a two-stage framework built on heterogeneous graph neural networks (GNNs) and prompt learning. Specifically, we construct a CDG heterogeneous graph, employ parameter-free metapath-guided message passing for high-order semantic capture, and optimize generalizable representations via a dual self-supervised objective (association prediction + feature reconstruction) during pretraining. For downstream tasks, task-specific learnable prompt vectors are introduced to adapt frozen pretrained representations to CD, CG and DG association prediction tasks via additive fusion, preserving core semantics while injecting task-specific biases. Comprehensive experiments demonstrate PPF-HGNN's state-of-the-art performance: AUC of 0.9633 (CD), 0.9939 (CG), and 0.9390 (DG), with F1-scores of 0.9157, 0.9668, and 0.8955 respectively-substantially outperforming six existing baselines. This work validates the pretrain-prompt-finetune paradigm for multi-task biomedical association prediction, providing a robust AI-driven tool to accelerate translational research and decipher complex CDG relationships. The source code is available at https://github.com/ike-zengxi/PPF-HGNN.
Zeng et al. (2026) studied this question.