22 citations · 31 across the 12 of their papers we have counts for
13 papers
BioScore: A Foundational Scoring Function For Diverse Biomolecular Complexes
Yuchen Zhu, Jihong Chen, Yitong Li +9
Structural assessment of biomolecular complexes is vital for translating molecular models into functional insights, shaping our understanding of biology and aiding drug discovery.…
A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention
Qun Su, Kai Zhu, Qiaolin Gou +11
Accurate atomistic biomolecular simulations are vital for disease mechanism understanding, drug discovery, and biomaterial design, but existing simulation methods exhibit significa…
Graph Neural Networks in Modern AI-aided Drug Discovery
Odin Zhang, Haitao Lin, Xujun Zhang +9
Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on m…
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…
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…
Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model
Jike Wang, Jianwen Feng, Yu Kang +16
Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks. We introduce AMP-Desi…
Deep Lead Optimization: Leveraging Generative AI for Structural Modification
Odin Zhang, Haitao Lin, Hui Zhang +7
The idea of using deep-learning-based molecular generation to accelerate discovery of drug candidates has attracted extraordinary attention, and many deep generative models have be…
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation
Lijun Liu, Jiali Yang, Jianfei Song +8
Recombinant adeno-associated virus (rAAV) vectors have revolutionized gene therapy, but their broad tropism and suboptimal transduction efficiency limit their clinical applications…
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Odin Zhang, Yufei Huang, Shichen Cheng +14
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pock…
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…