3 citations · 11 across the 12 of their papers we have counts for
12 papers
AgentFold: Closed-Loop Agentic Search for Protein Folding Model Design
Mingquan Liu, Jiangyu Chen, Hanqun Cao +9
Scientific LLM agents have shown promise in literature reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomously improve large, tightly cou…
Navigating committor landscape of biomolecules with a general pairwise interaction model
Jintu Zhang, Zichang Jin, Huifeng Zhao +5
Sampling rare conformation transitions between metastable states is a central challenge in atomistic simulations. While the committor function serve as an ideal reaction coordinate…
An accurate nucleic acid-small molecule docking framework via geometric deep learning with large-scale pretraining
Shi Li, Xujun Zhang, Mingquan Liu +5
Nucleic acids are increasingly recognized as therapeutic targets beyond conventional protein-centered drug discovery, yet accurate and efficient docking of small molecules to nucle…
ODesign: A World Model for Biomolecular Interaction Design
Odin Zhang, Xujun Zhang, Haitao Lin +34
Biomolecular interactions underpin almost all biological processes, and their rational design is central to programming new biological functions. Generative AI models have emerged…
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.…
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…
Token-Mol 1.0: Tokenized drug design with large language model
Jike Wang, Rui Qin, Mingyang Wang +17
Significant interests have recently risen in leveraging sequence-based large language models (LLMs) for drug design. However, most current applications of LLMs in drug discovery la…
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 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…
Delete: Deep Lead Optimization Enveloped in Protein Pocket through Unified Deleting Strategies and a Structure-aware Network
Haotian Zhang, Huifeng Zhao, Xujun Zhang +10
Drug discovery is a highly complicated process, and it is unfeasible to fully commit it to the recently developed molecular generation methods. Deep learning-based lead optimizatio…
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop
Tianyue Wang, Xujun Zhang, Odin Zhang +5
Protein loop modeling is the most challenging yet highly non-trivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab i…