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