22 citations · 34 across the 8 of their papers we have counts for
9 papers
AutoLoop: a novel autoregressive deep learning method for protein loop prediction with high accuracy
Tianyue Wang, Xujun Zhang, Langcheng Wang +12
Protein structure prediction is a critical and longstanding challenge in biology, garnering widespread interest due to its significance in understanding biological processes. A par…
ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddings
Zitai Kong, Yiheng Zhu, Yinlong Xu +7
The design of protein sequences with desired functionalities is a fundamental task in protein engineering. Deep generative methods, such as autoregressive models and diffusion mode…
SALM: Sequence-Structure Pre-trained Large Language Model for Comprehensive Antibody Representation Learning
Mingze Yin, Hanjing Zhou, Jialu Wu +8
Antibodies safeguard our health through their precise and potent binding to specific antigens, demonstrating promising therapeutic efficacy in the treatment of numerous diseases, i…
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges
Yiheng Zhu, Jialu Wu, Qiuyi Li +7
Inverse protein folding is a fundamental task in computational protein design, which aims to design protein sequences that fold into the desired backbone structures. While the deve…
Multi-Modal CLIP-Informed Protein Editing
Mingze Yin, Hanjing Zhou, Yiheng Zhu +8
Proteins govern most biological functions essential for life, but achieving controllable protein discovery and optimization remains challenging. Recently, machine learning-assisted…
Generative AI for Controllable Protein Sequence Design: A Survey
Yiheng Zhu, Zitai Kong, Jialu Wu +6
The design of novel protein sequences with targeted functionalities underpins a central theme in protein engineering, impacting diverse fields such as drug discovery and enzymatic…
Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation
Yue Wan, Jialu Wu, Tingjun Hou +2
Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with…
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
Yiheng Zhu, Zhenqiu Ouyang, Ben Liao +5
Molecular de novo design is a critical yet challenging task in scientific fields, aiming to design novel molecular structures with desired property profiles. Significant progress h…
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Yiheng Zhu, Jialu Wu, Chaowen Hu +4
Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical s…