3 citations · 8 across the 6 of their papers we have counts for
6 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…
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…