6 papers
Exploring the Alignment of Generation and Understanding in Protein Structure Modeling
Junde Xu, Yuansheng Huang, Zijun Gao +5
Understanding and generation are often treated as two separate paradigms in training deep neural networks, despite the fact that both are trained with closely related objectives su…
Reshaping Biomolecular Structure Prediction through Strategic Conformational Exploration with HelixFold-S1
Lihang Liu, Yang Liu, Xianbin Ye +6
Generating large ensembles of candidate conformations is standard for improving biomolecular structure prediction. Yet aimless sampling is inefficient and costly, producing many re…
Technical Report of HelixFold3 for Biomolecular Structure Prediction
Lihang Liu, Shanzhuo Zhang, Yang Xue +10
The AlphaFold series has transformed protein structure prediction with remarkable accuracy, often matching experimental methods. AlphaFold2, AlphaFold-Multimer, and the latest Alph…
Precise Antigen-Antibody Structure Predictions Enhance Antibody Development with HelixFold-Multimer
Jie Gao, Jing Hu, Lihang Liu +4
The accurate prediction of antigen-antibody structures is essential for advancing immunology and therapeutic development, as it helps elucidate molecular interactions that underlie…
Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models
Lihang Liu, Shanzhuo Zhang, Donglong He +12
Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). R…
HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights
Xiaomin Fang, Jie Gao, Jing Hu +4
While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field. This challenge i…