5 citations · 14 across the 19 of their papers we have counts for
10 papers · 1 filter
Equivariant Neural Primal-Dual Assignment for Maximum Common Edge Subgraphs
Jiaqing Xie, Yanchao Li, Zhuo Yang +3
Maximum common edge subgraph (MCES) matching finds a partial vertex correspondence between two labeled graphs that preserves as many labeled edges as possible. Molecular similarity…
Analytic-Walk Rotary Positional Encodings for Graphs
Jiaqing Xie, Yuxin Wang, Xipeng Qiu
Rotary position encodings make attention sensitive to relative position, but extending them to graphs requires choosing how graph structure enters the rotation. Previous works assi…
Disagree to Accelerate: Closing the Loop on Diffusion Feature Forecasts
Yanchao Li, Jiaqing Xie, Ben Gao +6
Training-free feature forecasting accelerates diffusion sampling by predicting features at skipped denoising steps. Recent work has mainly focused on designing stronger forecasters…
ABOPD: Antibody CDR Design via On-Policy Distillation
Zhuo Yang, Jiaying He, Jiaqing Xie +5
Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface. Recent protein generative model…
Eigenbasis-Independent Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces
Jiaqing Xie, Yuxin Wang
Spectral positional encodings (PEs) for \emph{directed} graphs face two obstacles: magnetic Laplacians require an Hermitian eigendecomposition per potential, and their com…
SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
Zhuo Yang, Jiaqing Xie, Shuaike Shen +13
Deep learning holds immense promise for spectroscopy, yet research and evaluation in this emerging field often lack standardized formulations. To address this issue, we introduce S…
SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning
Shuaike Shen, Jiaqing Xie, Zhuo Yang +6
Large language models have emerged as transformative tools in molecular science, demonstrating remarkable potential in molecular property prediction and de novo molecular design. H…
Benchmarking Positional Encodings for GNNs and Graph Transformers
Florian Grötschla, Jiaqing Xie, Roger Wattenhofer
Positional Encodings (PEs) are essential for injecting structural information into Graph Neural Networks (GNNs), particularly Graph Transformers, yet their empirical impact remains…
DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning
Jiaqing Xie, Tianfan Fu
Deep learning has deeply influenced protein science, enabling breakthroughs in predicting protein properties, higher-order structures, and molecular interactions. This paper introd…
Could Chemical LLMs benefit from Message Passing
Jiaqing Xie, Ziheng Chi
Pretrained language models (LMs) showcase significant capabilities in processing molecular text, while concurrently, message passing neural networks (MPNNs) demonstrate resilience…