Artemis: Harnessing Knowledge Graphs for Next-Generation Drug Target Prioritization

Knowledge graphs (KGs) have become an important asset in biomedical research and drug discovery by enabling the structured integration of heterogeneous biological knowledge. When combined with machine learning (ML), KGs support the identification of novel drug-target relationships, but existing approaches are often KG-centric, relying primarily on graph structure and embeddings while overlooking disease-specific biological and clinical context. Moreover, many high-impact applications depend on proprietary KG infrastructures, limiting accessibility for the broader research community. Here, we introduce Artemis, a practical and generalisable machine-learning framework for indication-aware target prioritisation that integrates public biomedical KGs with clinical evidence from the ChEMBL database. Artemis derives graph-based representations of clinically validated drug targets from multiple publicly available KGs and augments them with disease-relevant clinical features from ChEMBL. This hybrid feature space is used to train supervised ML models across seven disease indications, with performance assessed via cross-validation and guided parameter optimisation. The framework is further evaluated on emerging breast cancer targets reported at the San Antonio Breast Cancer Symposium 2024, demonstrating its ability to prioritise novel candidates. Overall, this work demonstrates that publicly available KGs can be used for actionable, translational target discovery when coupled with clinical data. Artemis provides an accessible, scalable, and cost-efficient alternative to proprietary KG platforms. Thereby offering a practical solution for researchers seeking to prioritise therapeutic targets in real-world drug discovery settings. Key PointsO_LIKG applications can support the identification of novel drug-target relationships but rely primarily on graph structure while overlooking disease-specific biological and clinical context. C_LIO_LIArtemis performs indication-aware target prioritisation that integrates public biomedical KGs with clinical evidence from the ChEMBL database. C_LIO_LIArtemis is evaluated on emerging breast cancer targets reported at the San Antonio Breast Cancer Symposium 2024, demonstrating its ability to prioritise novel candidates. C_LIO_LIArtemis provides an accessible, scalable, and cost-efficient alternative to proprietary KG platforms offering a practical solution for researchers seeking to prioritise therapeutic targets in real-world drug discovery settings. C_LI