# MuCoS: Efficient Drug Target Discovery via Multi Context Aware Sampling in Knowledge Graphs

arXiv: https://arxiv.org/abs/2503.08075 · HTML: https://mlsanity.com/paper/2503.08075/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [A Contextualized BERT model for Knowledge Graph Completion](https://mlsanity.com/paper/2412.11016/) (2024)
- [MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion](https://mlsanity.com/paper/2503.03091/) (2025)

## Built on by (most cited first, 2 of 2)

- [Evaluating Knowledge Graph Complexity via Semantic, Spectral, and Structural Metrics for Link Prediction](https://mlsanity.com/paper/2508.15291/) (2025)
- [Evaluating Cumulative Spectral Gradient as a Complexity Measure](https://mlsanity.com/paper/2509.02399/) (2025)

## Abstract (arXiv)

Accurate prediction of drug target interactions is critical for accelerating drug discovery and elucidating complex biological mechanisms. In this work, we frame drug target prediction as a link prediction task on heterogeneous biomedical knowledge graphs (KG) that integrate drugs, proteins, diseases, pathways, and other relevant entities. Conventional KG embedding methods such as TransE and ComplEx SE are hindered by their reliance on computationally intensive negative sampling and their limited generalization to unseen drug target pairs. To address these challenges, we propose Multi Context Aware Sampling (MuCoS), a novel framework that prioritizes high-density neighbours to capture salient structural patterns and integrates these with contextual embeddings derived from BERT. By unifying structural and textual modalities and selectively sampling highly informative patterns, MuCoS circumvents the need for negative sampling, significantly reducing computational overhead while enhancing predictive accuracy for novel drug target associations and drug targets. Extensive experiments on the KEGG50k dataset demonstrate that MuCoS outperforms state-of-the-art baselines, achieving up to a 13% improvement in mean reciprocal rank (MRR) in predicting any relation in the dataset and a 6% improvement in dedicated drug target relation prediction.