arXiv:2609.06779v1 Announce Type: cross
Abstract: Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cos...
By Zijie Liu, Hongxuan Li, Zhen Tan, Jinhao Duan, Baixiang Huang, Zunpeng Liu, Kai Shu, Tianlong Chen
OptimusKG is a multimodal biomedical labeled property graph that integrates structured and semi‑structured resources to preserve detailed, type‑specific metadata across molecular, anatomical, clinical, and environmental domains. The graph contains nearly 191,000 nodes, over 21.8 million edges, and more than 67 million property instances derived from 18 ontologies, with a top‑level schema that enforces node and edge constraints while retaining granular provenance. Validation using the PaperQA3 agent found that 70.0% of sampled edges are supported by literature evidence, and the graph offers a standardized resource for machine learning, knowledge‑grounded retrieval, and hypothesis generation in biomedical research.
By Lucas Vittor, Ayush Noori, I\~naki Arango, Joaqu\'in Polonuer, Sam Rodriques, Andrew White, David A. Clifton, Marinka Zitnik
arXiv:2608.30556v1 Announce Type: new
Abstract: Path-finding over knowledge graphs has become an effective way to ground LLM reasoning on multi-hop questions. However, biomedical QA introduces two di...
By Jun Hyeong Kim, Dongki Kim, Yinhua Piao, Sung Ju Hwang
arXiv:2510.07289v2 Announce Type: replace
Abstract: Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated s...
By Xingtong Yu, Chang Zhou, Xinming Zhang, Yuan Fang
HyGRAIL is a framework for discovering scientific hypotheses in incomplete knowledge graphs by combining a graph neural network (GNN) triage with large language model (LLM) review. The GNN scores candidate hypotheses and routes only ambiguous cases to the LLM, which receives structured evidence from the graph converted into natural language. Experiments on MatKG show HyGRAIL achieves the highest F1 score, improves over baselines, and cuts LLM calls by over 54%.
By Yihang Sun, Zhihan Zhu, Zhiyuan Jiang, Jingyi Ge, Zixuan Li, Jiaxuan You
arXiv:2609.37555v1 Announce Type: new
Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical st...
By Noel Suarez-Barro, Manuel Lama, Juan C. Vidal