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20232026
most citedLearning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction

27 citations · 29 across the 14 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

Learning Molecular Representations from Cellular Phenotypes with Structure Preservation

Xuan Lin, Jingyu Sheng, Tengfei Ma +2

Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses. However, existing multimodal representation learnin…

cs.LG2026

CryoProt: A Protein Pretraining Framework with Cross-Box Interactions on Cryo-EM Density Maps

Dan Luo, Xuan Lin, Peng Zhou +4

Despite the growing availability of cryo-electron microscopy (cryo-EM) density maps, effectively leveraging them for protein representation remains challenging. First, current meth…

cs.LG2025

DeepDR: an integrated deep-learning model web server for drug repositioning

Shuting Jin, Yi Jiang, Yimin Liu +5

Background: Identifying new indications for approved drugs is a complex and time-consuming process that requires extensive knowledge of pharmacology, clinical data, and advanced co…

cs.LG2025

MolBridge: Atom-Level Joint Graph Refinement for Robust Drug-Drug Interaction Event Prediction

Xuan Lin, Aocheng Ding, Tengfei Ma +2

Drug combinations offer therapeutic benefits but also carry the risk of adverse drug-drug interactions (DDIs), especially under complex molecular structures. Accurate DDI event pre…

cs.LG2025

AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery

Yifan Dai, Xuanbai Ren, Tengfei Ma +4

Accurate molecular property prediction (MPP) is a critical step in modern drug development. However, the scarcity of experimental validation data poses a significant challenge to A…

cs.LG2024

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…