8 citations · 17 across the 8 of their papers we have counts for
8 papers
Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling
Fang Wu, Stan Z. Li
Protein-protein interaction (PPI) represents a central challenge within the biology field, and accurately predicting the consequences of mutations in this context is crucial for dr…
MergeDNA: Context-aware Genome Modeling with Dynamic Tokenization through Token Merging
Siyuan Li, Kai Yu, Anna Wang +7
Modeling genomic sequences faces two unsolved challenges: the information density varies widely across different regions, while there is no clearly defined minimum vocabulary unit.…
Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification
Zicheng Liu, Siyuan Li, Zhiyuan Chen +6
The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. Although modern biological pre-tr…
A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design
Fang Wu, Stan Z. Li
Therapeutic antibodies are an essential and rapidly expanding drug modality. The binding specificity between antibodies and antigens is decided by complementarity-determining regio…
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…
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,…
InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?
Fang Wu, Stan Z. Li
The integration of sentences poses an intriguing challenge within the realm of NLP, but it has not garnered the attention it deserves. Existing methods that focus on sentence arran…