Chen Research Lab | Home

University of Missouri

Computational RNA Biophysics

Advances in machine-learning approaches to RNA-targeted drug design

Our team (VFOLD) was ranked #1 in CASP16 (2024) RNA categories (https://predictioncenter.org/casp16/zscores_rna.cgi).

Our team (VFOLD) was ranked #1 in CASP16 (2024) RNA categories (https://predictioncenter.org/casp16/zscores_rna.cgi).

Our team was ranked #2 in CASP15 (2022) RNA categories (https://predictioncenter.org/casp15/zscores_RNA.cgi).

Machine learning-inferred and energy landscape-guided analyses reveal kinetic determinants of CRISPR/Cas9 gene editing

Kinetic pathway of HIV-1 TAR cotranscriptional folding

Advancing RNA 3D Structure Prediction: Exploring Hierarchical and Hybrid Approaches in CASP15

RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling

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Research Interests

RNA 2D structure folding

We develop physics-based 2D models to predict RNA structure and folding thermodynamics from the sequence.

RNA 3D structure folding

We develop computational tools to predict all-atom 3D structures of RNA from the sequence.

RNA-ligand docking

We conduct RNA-ligand docking researches which can be crucial in selecting small molecules as drug candidates.

CRISPR-Cas9 system

We develop mechanistic models to investigate CRISPR-Cas9 gene editing.

RNA-Deep learning

We use deep learning methods to predict RNA-metal ion interactions

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