Youhan Lee

The paper describes a Kaggle competition for RNA 3D structure prediction, showing that template-based methods can outperform deep learning approaches.

The paper introduces a benchmark for evaluating protein ensemble generators across ordered and disordered proteins and presents a new model that improves performance on disordered regions.

This study shows that accurate antibody loop structure prediction enables the design of target-specific antibodies without prior structural templates.

DNDesign improves protein inverse folding by integrating denoising training to better capture the energy landscape, enhancing performance across multiple models.

ShapeProt generates protein sequences from 3D shape contexts using a surface-based generative model, overcoming limitations of existing sequence and structure-focused approaches.