1 code implementation • 18 Apr 2024 • Yanru Qu, Keyue Qiu, Yuxuan Song, Jingjing Gong, Jiawei Han, Mingyue Zheng, Hao Zhou, Wei-Ying Ma
Generative models for structure-based drug design (SBDD) have shown promising results in recent years.
1 code implementation • 17 Mar 2024 • Yuxuan Song, Jingjing Gong, Yanru Qu, Hao Zhou, Mingyue Zheng, Jingjing Liu, Wei-Ying Ma
Advanced generative model (e. g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry.
no code implementations • 22 Feb 2024 • Yuwei Yang, Siqi Ouyang, Xueyu Hu, Mingyue Zheng, Hao Zhou, Lei LI
We develop a novel 3D graph editing model to generate molecules using fragments, and pre-train this model on abundant 3D ligands for learning target-independent properties.
no code implementations • 29 Sep 2021 • Yuwei Yang, Siqi Ouyang, Meihua Dang, Mingyue Zheng, Lei LI, Hao Zhou
Deep learning models have been widely used in automatic drug design.
2 code implementations • Bioinformatics 2020 • Lifan Chen, Xiaoqin Tan, Dingyan Wang, Feisheng Zhong, Xiaohong Liu, Tianbiao Yang, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, Mingyue Zheng
Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI.
Ranked #2 on Drug Discovery on LIT-PCBA(ESR1_ant)