5 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…
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…
Unifying Sequences, Structures, and Descriptions for Any-to-Any Protein Generation with the Large Multimodal Model HelixProtX
Zhiyuan Chen, Tianhao Chen, Chenggang Xie +4
Proteins are fundamental components of biological systems and can be represented through various modalities, including sequences, structures, and textual descriptions. Despite the…
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…