Table 12 summarises the datasets used in various human mesh recovery algorithms.

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.

Drug-target interactions are critical for understanding biological processes and advancing drug discovery. However, traditional methods such as ComplEx-SE, TransE, and DistMult struggle with unseen relationships and negative triplets, which limits their effectiveness in drug-target prediction. To address these challenges, we propose Multi-Context-Aware Sampling (MuCoS), an efficient and positively accurate method for drug-target prediction. MuCoS reduces computational complexity by prioritizing neighbors of higher density to capture informative structural patterns. These optimized neighborhood representations are integrated with BERT, enabling contextualized embeddings for accurate prediction of missing relationships or tail entities. MuCoS avoids the need for negative triplet sampling, reducing computation while improving performance over unseen entities and relations. Experiments on the KEGG50k biomedical dataset show that MuCoS improved over existing models by 13% on MRR, 7% on Hits@1, 4% on Hits@3, and 18% on Hits@10 for the general relationship, and by 6% on MRR, 1% on Hits@1, 3% on Hits@3, and 12% on Hits@10 for prediction of drug-target relationship.