Zobrazeno 1 - 10
of 408
pro vyhledávání: '"Qiang, Bo"'
Developing an efficient sampler capable of generating independent and identically distributed (IID) samples from a Boltzmann distribution is a crucial challenge in scientific research, e.g. molecular dynamics. In this work, we intend to learn neural
Externí odkaz:
http://arxiv.org/abs/2409.09787
Proteolysis targeting chimeras (PROTACs) are small molecules that trigger the breakdown of traditionally ``undruggable'' proteins by binding simultaneously to their targets and degradation-associated proteins. A key challenge in their rational design
Externí odkaz:
http://arxiv.org/abs/2405.06654
Autor:
Liu, Ningfeng, Yu, Jie, Xiu, Siyu, Zhao, Xinfang, Lin, Siyu, Qiang, Bo, Zheng, Ruqiu, Jin, Hongwei, Zhang, Liangren, Liu, Zhenming
Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology. However, challenges remain in multi-objective generation, model adaptability, and prac
Externí odkaz:
http://arxiv.org/abs/2404.06691
Autor:
Gao, Bowen, Ren, Minsi, Ni, Yuyan, Huang, Yanwen, Qiang, Bo, Ma, Zhi-Ming, Ma, Wei-Ying, Lan, Yanyan
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular generative methods and docking scores
Externí odkaz:
http://arxiv.org/abs/2403.12987
Structure-based drug design (SBDD) stands at the forefront of drug discovery, emphasizing the creation of molecules that target specific binding pockets. Recent advances in this area have witnessed the adoption of deep generative models and geometric
Externí odkaz:
http://arxiv.org/abs/2311.12035
Autor:
Gao, Bowen, Qiang, Bo, Tan, Haichuan, Ren, Minsi, Jia, Yinjun, Lu, Minsi, Liu, Jingjing, Ma, Weiying, Lan, Yanyan
Virtual screening, which identifies potential drugs from vast compound databases to bind with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditional docking methods are highly time-consuming, and can only work with
Externí odkaz:
http://arxiv.org/abs/2310.06367
Autor:
Qiang, Bo, Song, Yuxuan, Xu, Minkai, Gong, Jingjing, Gao, Bowen, Zhou, Hao, Ma, Weiying, Lan, Yanyan
Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate molecules in atom resolution and ignore intrinsic local structures such
Externí odkaz:
http://arxiv.org/abs/2305.13266
Autor:
Qiang, Bo, Zhou, Yiran, Ding, Yuheng, Liu, Ningfeng, Song, Song, Zhang, Liangren, Huang, Bo, Liu, Zhenming
Chemical reactions are the fundamental building blocks of drug design and organic chemistry research. In recent years, there has been a growing need for a large-scale deep-learning framework that can efficiently capture the basic rules of chemical re
Externí odkaz:
http://arxiv.org/abs/2303.06965
Autor:
Li, Yingjie, Wang, Xiaoyu, Xu, Shenao, Qiang, Bo, Shi, Wenxu, Gu, Jing, Yu, Moxin, Li, Chunxi
Publikováno v:
In Separation and Purification Technology 19 January 2025 353 Part C
Autor:
Wang Jun-Ling, Liu Ming-Sheng, Fu Yu-Dong, Kan Qiang-Bo, Li Chun-Yan, Ma Rong, Fang Zhe-Wei, Liu Hong-Xia, Li Meng-Xian, Lv Jia-Ling, Sang Peng, Zhang Chao, Li Hong-Wei
Publikováno v:
Open Life Sciences, Vol 18, Iss 1, Pp 1-20 (2023)
Externí odkaz:
https://doaj.org/article/32d5ee97b9564a8a8bfba82e81401ad4