RNA Structure
Understanding RNA structure through sequence, topology, evolutionary constraints, and physical principles.
TriRNASP · tpFormer
Research
Structure, design,
and physical principles.
My research interests connect the prediction and design of RNA with the physical and statistical principles that organize molecular structure.
RNA Design
Designing sequences for desired three-dimensional structures, with function as a longer-term question.
DS3dRNA
Computational Biophysics
Using structural statistics and physical models to study molecular organization and structural scoring.
TriRNASP · DS3dRNA
AI for Molecular Science
Exploring models that connect structural, topological, evolutionary, and physical information.
tpFormer · Future directions
01From evidence to questions
Published work on TriRNASP examines RNA structural scoring through three-body statistical information. DS3dRNA provides public software for structure-conditioned sequence design. tpFormer is an active investigation of topology-aware structure modeling.
These projects approach related questions from different directions: evaluating a proposed structure, finding sequences for a target structure, and learning representations for structure prediction.
02Research-interest map
Connected ideas across current work and future questions.
Size indicates conceptual emphasis. Groups identify research status.
Core focus
- RNA
Published work
- RNA Structure
- Computational Biophysics
- RNA 3D Structure
- Statistical Potentials
- Three-body Interactions
- Structural Biology
- Tertiary Structure
Active projects
- RNA Design
- Topology-aware Learning
- Evolution
- MSA
- Machine Learning
- Structure Prediction
- Contact Map
- Topology
- de novo Design
- Secondary Structure
- Deep Learning
Exploratory interests
- Synthetic Biology
- RNA Foundation Models
- Energy Landscape
- Functional RNA
- Geometric Learning
- Physics-informed AI
- Ribozyme
- RNA Engineering
- Molecular Design
Current directions
From structural principles
to molecular design.
Topology-aware RNA modeling and evolutionary information guide current questions in structure prediction. Longer-term interests include RNA foundation models, de novo design, and AI-guided RNA engineering grounded in physical and statistical potentials.
Explore research directions →