MGPT: A Multi‐task Graph Prompt Learning Framework for Drug Discovery

Abstract Predicting accurate drug associations, including drug‐target interactions, drug side effects, and drug‐disease relationships, is crucial in biomedical research and precision medicine. Recently, the research community is increasingly adopted graph representation learning methods to investigate drug associations. However, translating advancements in graph pre‐training to the domain of drug development faces significant challenges, particularly in multi‐task learning and few‐shot scenarios. A unified Multi‐task Graph PrompT (MGPT) learning model is proposed providing generalizable and robust graph representations for few‐shot drug association prediction. MGPT constructs a heterogeneous graph network using different entity pairs as nodes and utilizes self‐supervised contrastive learning of sub‐graphs in pre‐training. For downstream tasks, MGPT employs learnable functional prompts, embedded with task‐specific knowledge, to enable robust performance across a range of tasks. MGPT demonstrates the ability of seamless task switching and outperforms competitive approaches in few‐shot scenarios. MGPT emerges as a robust solution to the complexities of multi‐task learning and the challenges associated with limited data in drug development.