Dongliang Hou

TopoFlow: Evolutionarily Conditioned Flow Matching for Protein Conformational Ensemble Generation

TopoFlow generates protein conformational ensembles using evolutionary and structural information, improving benchmark metrics and aligning with experimental data.

Exploring protein conformational ensembles using evolutionary conditional diffusion

This paper introduces DiffEnsemble, a diffusion-based framework for modeling protein conformational ensembles, showing improved performance over existing methods.

ProSiteHunter: A unified framework for sequence-based prediction of protein-nucleic acid and protein-protein binding sites

ProSiteHunter improves protein binding site prediction by integrating sequence-based features and a fine-tuned language model, achieving better performance than existing methods.

High-accuracy protein complex structure modeling based on sequence-derived structure complementarity

This paper introduces DeepSCFold, a method for improving protein complex structure prediction by using sequence-based deep learning to model inter-chain interactions, achieving higher accuracy than existing methods.

High-accuracy structure modeling for antibody-antigen complexes

DeepAAAssembly improves antibody-antigen complex modeling by integrating dynamic interactions, outperforming AlphaFold3 on a benchmark set.

MultiSAAI: Sequence-informed Antibody-Antigen Interactions Prediction using Multi-scale Deep Learning

MultiSAAI predicts antibody-antigen interactions using a multi-scale deep learning framework, achieving high AUROC scores on benchmark datasets.