Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Wang, Yingze"'
Autor:
Wang, Yingze, Sun, Kunyang, Li, Jie, Guan, Xingyi, Zhang, Oufan, Bagni, Dorian, Head-Gordon, Teresa
Development of scoring functions (SFs) used to predict protein-ligand binding energies requires high-quality 3D structures and binding assay data, and often relies on the PDBBind dataset for training and testing their parameters. In this work we show
Externí odkaz:
http://arxiv.org/abs/2411.01223
Autor:
Gong, Zhenzhen, Hashash, Omar, Wang, Yingze, Cui, Qimei, Ni, Wei, Saad, Walid, Sakaguchi, Kei
In this paper, a novel joint energy and age of information (AoI) optimization framework for IoT devices in a non-stationary environment is presented. In particular, IoT devices that are distributed in the real-world are required to efficiently utiliz
Externí odkaz:
http://arxiv.org/abs/2312.00334
Autor:
Li, Jie, Guan, Xingyi, Zhang, Oufan, Sun, Kunyang, Wang, Yingze, Bagni, Dorian, Head-Gordon, Teresa
Many physics-based and machine-learned scoring functions (SFs) used to predict protein-ligand binding free energies have been trained on the PDBBind dataset. However, it is controversial as to whether new SFs are actually improving since the general,
Externí odkaz:
http://arxiv.org/abs/2308.09639
Autor:
Zeng, Jinzhe, Zhang, Duo, Lu, Denghui, Mo, Pinghui, Li, Zeyu, Chen, Yixiao, Rynik, Marián, Huang, Li'ang, Li, Ziyao, Shi, Shaochen, Wang, Yingze, Ye, Haotian, Tuo, Ping, Yang, Jiabin, Ding, Ye, Li, Yifan, Tisi, Davide, Zeng, Qiyu, Bao, Han, Xia, Yu, Huang, Jiameng, Muraoka, Koki, Wang, Yibo, Chang, Junhan, Yuan, Fengbo, Bore, Sigbjørn Løland, Cai, Chun, Lin, Yinnian, Wang, Bo, Xu, Jiayan, Zhu, Jia-Xin, Luo, Chenxing, Zhang, Yuzhi, Goodall, Rhys E. A., Liang, Wenshuo, Singh, Anurag Kumar, Yao, Sikai, Zhang, Jingchao, Wentzcovitch, Renata, Han, Jiequn, Liu, Jie, Jia, Weile, York, Darrin M., E, Weinan, Car, Roberto, Zhang, Linfeng, Wang, Han
Publikováno v:
J. Chem. Phys. 159, 054801 (2023)
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the f
Externí odkaz:
http://arxiv.org/abs/2304.09409
Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery. Recently, prosperous progress has been made in molec
Externí odkaz:
http://arxiv.org/abs/2209.15101
Publikováno v:
In International Communications in Heat and Mass Transfer December 2024 159 Part A
Publikováno v:
In International Journal of Heat and Mass Transfer 1 December 2024 234
Autor:
Li, Jie, Zhang, Oufan, Wang, Yingze, Sun, Kunyang, Guan, Xingyi, Bagni, Dorian, Haghighatlari, Mojtaba, Kearns, Fiona L., Parks, Conor, Amaro, Rommie E., Head-Gordon, Teresa
The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules
Externí odkaz:
http://arxiv.org/abs/2110.01806
Autor:
Liu, Yingnan, Zhang, Guofang, Li, Qi, Chen, Jiaxin, Luo, Wenhe, Li, Xuejin, Suo, Xiaoman, Li, Su, Xu, Yaqing, Liu, Tinghao, Yuan, Feng, Liu, Fangfang, Zeng, Yanqiao, Wang, Yingze, Li, Yang
Publikováno v:
In Chemical Engineering Journal 15 June 2024 490
Publikováno v:
In Journal of Thermal Biology January 2024 119