26 citations · 81 across the 13 of their papers we have counts for
10 papers · 1 filter
Scalable Peptide Design via Memory-Efficient Equivariant Transformer
Rui Jiao, Xiangzhe Kong, Yinjun Jia +4
Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints. Latent generative frameworks offer an effective route for this probl…
h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network
Yanru Qu, Yijie Zhang, Wenjuan Tan +6
Accurate molecular representations are critical for drug discovery, and a central challenge lies in capturing the chemical environment of molecular fragments, as key interactions,…
Peptide2Mol: A Diffusion Model for Generating Small Molecules as Peptide Mimics for Targeted Protein Binding
Xinheng He, Yijia Zhang, Haowei Lin +4
Structure-based drug design has seen significant advancements with the integration of artificial intelligence (AI), particularly in the generation of hit and lead compounds. Howeve…
Latent Retrieval Augmented Generation of Cross-Domain Protein Binders
Zishen Zhang, Xiangzhe Kong, Wenbing Huang +1
Designing protein binders targeting specific sites, which requires to generate realistic and functional interaction patterns, is a fundamental challenge in drug discovery. Current…
Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
Dapeng Jiang, Xiangzhe Kong, Jiaqi Han +6
Cyclic peptides, characterized by geometric constraints absent in linear peptides, offer enhanced biochemical properties, presenting new opportunities to address unmet medical need…
UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
Xiangzhe Kong, Zishen Zhang, Ziting Zhang +5
The design of target-specific molecules such as small molecules, peptides, and antibodies is vital for biological research and drug discovery. Existing generative methods are restr…
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Jiaqi Han, Jiacheng Cen, Liming Wu +13
Geometric graphs are a special kind of graph with geometric features, which are vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physi…
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning
Rui Jiao, Xiangzhe Kong, Li Zhang +5
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications. However, prior efforts typically focus on pretraining models i…
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning
Xiangzhe Kong, Wenbing Huang, Yang Liu
Many processes in biology and drug discovery involve various 3D interactions between molecules, such as protein and protein, protein and small molecule, etc. Given that different m…
Molecule Generation by Principal Subgraph Mining and Assembling
Xiangzhe Kong, Wenbing Huang, Zhixing Tan +1
Molecule generation is central to a variety of applications. Current attention has been paid to approaching the generation task as subgraph prediction and assembling. Nevertheless,…