8 papers
OpenDDI: A Comprehensive Benchmark for DDI Prediction
Xinmo Jin, Bowen Fan, Xunkai Li +9
Drug-Drug Interactions (DDIs) significantly influence therapeutic efficacy and patient safety. As experimental discovery is resource-intensive and time-consuming, efficient computa…
The Patient is not a Moving Document: A World Model Training Paradigm for Longitudinal EHR
Irsyad Adam, Zekai Chen, David Laprade +5
Large language models (LLMs) trained with next-word-prediction have achieved success as clinical foundation models. Representations from these language backbones yield strong linea…
LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
Xunkai Li, Zhengyu Wu, Zekai Chen +6
Recently, the rapid advancement of multimodal domains has driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multimodal-attributed graphs. This…
DANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphs
Zekai Chen, Haodong Lu, Xunkai Li +5
Federated graph learning (FGL) enables collaborative training on graph data across multiple clients. With the rise of large language models (LLMs), textual attributes in FGL graphs…
BoostFGL: Boosting Fairness in Federated Graph Learning
Zekai Chen, Kairui Yang, Xunkai Li +6
Federated graph learning (FGL) enables collaborative training of graph neural networks (GNNs) across decentralized subgraphs without exposing raw data. While existing FGL methods o…
MagicDock: Toward Docking-oriented De Novo Ligand Design via Gradient Inversion
Zekai Chen, Xunkai Li, Sirui Zhang +6
De novo ligand design is a fundamental task that seeks to generate protein or molecule candidates that can effectively dock with protein receptors and achieve strong binding affini…