11 papers
Discovering the Representation Bottleneck of Graph Neural Networks
Fang Wu, Siyuan Li, Stan Z. Li
Graph neural networks (GNNs) rely mainly on the message-passing paradigm to propagate node features and build interactions, and different graph learning problems require different…
Tokenizing Electron Cloud in Protein-Ligand Interaction Learning
Haitao Lin, Odin Zhang, Jia Xu +6
The affinity and specificity of protein-molecule binding directly impact functional outcomes, uncovering the mechanisms underlying biological regulation and signal transduction. Mo…
USTEP: Spatio-Temporal Predictive Learning under A Unified View
Cheng Tan, Jue Wang, Zhangyang Gao +2
Spatio-temporal predictive learning plays a crucial role in self-supervised learning, with wide-ranging applications across a diverse range of fields. Previous approaches for tempo…
dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen
Cheng Tan, Yijie Zhang, Zhangyang Gao +6
The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing computational methods in antibody design of…
A Simple yet Effective DDG Predictor is An Unsupervised Antibody Optimizer and Explainer
Lirong Wu, Yunfan Liu, Haitao Lin +4
The proteins that exist today have been optimized over billions of years of natural evolution, during which nature creates random mutations and selects them. The discovery of funct…
FlowTS: Time Series Generation via Rectified Flow
Yang Hu, Xiao Wang, Zezhen Ding +7
Diffusion-based models have significant achievements in time series generation but suffer from inefficient computation: solving high-dimensional ODEs/SDEs via iterative numerical s…