105 citations · 133 across the 8 of their papers we have counts for
8 papers
Empowering Chemical Structures with Biological Insights for Scalable Phenotypic Virtual Screening
Xiaoqing Lian, Pengsen Ma, Tengfeng Ma +9
Motivation: The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade-off: structural screening offers scalabi…
From Knowledge to Treatment: Large Language Model Assisted Biomedical Concept Representation for Drug Repurposing
Chengrui Xiang, Tengfei Ma, Xiangzheng Fu +3
Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical…
ImageDDI: Image-enhanced Molecular Motif Sequence Representation for Drug-Drug Interaction Prediction
Yuqin He, Tengfei Ma, Chaoyi Li +6
To mitigate the potential adverse health effects of simultaneous multi-drug use, including unexpected side effects and interactions, accurately identifying and predicting drug-drug…
SDN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
Tengfei Ma, Yujie Chen, Liang Wang +3
Inductive Knowledge Graph Completion (KGC) aims to infer missing facts between newly emerged entities within knowledge graphs (KGs), posing a significant challenge. While recent st…
MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs
Zhixiang Cheng, Hongxin Xiang, Pengsen Ma +9
Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and ma…
KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Tengfei Ma, Xiang song, Wen Tao +6
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations…
Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
Tengfei Ma, Yujie Chen, Wen Tao +9
Molecular interaction prediction plays a crucial role in forecasting unknown interactions between molecules, such as drug-target interaction (DTI) and drug-drug interaction (DDI),…
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Xuan Lin, Lichang Dai, Yafang Zhou +9
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially…