42 citations · 75 across the 9 of their papers we have counts for
9 papers
Instructor-inspired Machine Learning for Robust Molecular Property Prediction
Fang Wu, Shuting Jin, Siyuan Li +1
Machine learning catalyzes a revolution in chemical and biological science. However, its efficacy heavily depends on the availability of labeled data, and annotating biochemical da…
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
Fang Wu, Siyuan Li, Xurui Jin +4
The success of graph neural networks (GNNs) provokes the question about explainability: ``Which fraction of the input graph is the most determinant of the prediction?'' Particularl…
Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks
Fang Wu, Lirong Wu, Dragomir Radev +2
Geometric deep learning has recently achieved great success in non-Euclidean domains, and learning on 3D structures of large biomolecules is emerging as a distinct research area. H…
Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
Siyuan Li, Di Wu, Fang Wu +2
Masked image modeling, an emerging self-supervised pre-training method, has shown impressive success across numerous downstream vision tasks with Vision transformers. Its underlyin…
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…
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations
Fang Wu, Stan Z. Li
Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles. Recently deep learning models become a popular way to acce…
Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
Fang Wu, Shuting Jin, Yinghui Jiang +7
The latest biological findings observe that the traditional motionless 'lock-and-key' theory is not generally applicable because the receptor and ligand are constantly moving. None…
Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport
Fang Wu, Nicolas Courty, Shuting Jin +1
Training data are usually limited or heterogeneous in many chemical and biological applications. Existing machine learning models for chemistry and materials science fail to consid…
Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs
Fang Wu, Dragomir Radev, Stan Z. Li
Procuring expressive molecular representations underpins AI-driven molecule design and scientific discovery. The research mainly focuses on atom-level homogeneous molecular graphs,…