Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Shi, Chence"'
Autor:
Lu, Jiarui, Chen, Xiaoyin, Lu, Stephen Zhewen, Shi, Chence, Guo, Hongyu, Bengio, Yoshua, Tang, Jian
Proteins adopt multiple structural conformations to perform their diverse biological functions, and understanding these conformations is crucial for advancing drug discovery. Traditional physics-based simulation methods often struggle with sampling e
Externí odkaz:
http://arxiv.org/abs/2410.18403
Designing novel proteins that bind to small molecules is a long-standing challenge in computational biology, with applications in developing catalysts, biosensors, and more. Current computational methods rely on the assumption that the binding pose o
Externí odkaz:
http://arxiv.org/abs/2409.12080
The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations in silico. Recently, generative models has been leveraged as a surroga
Externí odkaz:
http://arxiv.org/abs/2402.10433
Proteins are macromolecules that perform essential functions in all living organisms. Designing novel proteins with specific structures and desired functions has been a long-standing challenge in the field of bioengineering. Existing approaches gener
Externí odkaz:
http://arxiv.org/abs/2210.08761
In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on blind flexible selfdocking, where we aim to predict the positions, orientations and conformations of d
Externí odkaz:
http://arxiv.org/abs/2210.06069
Autor:
Yuan, Sha, Zhao, Hanyu, Zhao, Shuai, Leng, Jiahong, Liang, Yangxiao, Wang, Xiaozhi, Yu, Jifan, Lv, Xin, Shao, Zhou, He, Jiaao, Lin, Yankai, Han, Xu, Liu, Zhenghao, Ding, Ning, Rao, Yongming, Gao, Yizhao, Zhang, Liang, Ding, Ming, Fang, Cong, Wang, Yisen, Long, Mingsheng, Zhang, Jing, Dong, Yinpeng, Pang, Tianyu, Cui, Peng, Huang, Lingxiao, Liang, Zheng, Shen, Huawei, Zhang, Hui, Zhang, Quanshi, Dong, Qingxiu, Tan, Zhixing, Wang, Mingxuan, Wang, Shuo, Zhou, Long, Li, Haoran, Bao, Junwei, Pan, Yingwei, Zhang, Weinan, Yu, Zhou, Yan, Rui, Shi, Chence, Xu, Minghao, Zhang, Zuobai, Wang, Guoqiang, Pan, Xiang, Li, Mengjie, Chu, Xiaoyu, Yao, Zijun, Zhu, Fangwei, Cao, Shulin, Xue, Weicheng, Ma, Zixuan, Zhang, Zhengyan, Hu, Shengding, Qin, Yujia, Xiao, Chaojun, Zeng, Zheni, Cui, Ganqu, Chen, Weize, Zhao, Weilin, Yao, Yuan, Li, Peng, Zheng, Wenzhao, Zhao, Wenliang, Wang, Ziyi, Zhang, Borui, Fei, Nanyi, Hu, Anwen, Ling, Zenan, Li, Haoyang, Cao, Boxi, Han, Xianpei, Zhan, Weidong, Chang, Baobao, Sun, Hao, Deng, Jiawen, Zheng, Chujie, Li, Juanzi, Hou, Lei, Cao, Xigang, Zhai, Jidong, Liu, Zhiyuan, Sun, Maosong, Lu, Jiwen, Lu, Zhiwu, Jin, Qin, Song, Ruihua, Wen, Ji-Rong, Lin, Zhouchen, Wang, Liwei, Su, Hang, Zhu, Jun, Sui, Zhifang, Zhang, Jiajun, Liu, Yang, He, Xiaodong, Huang, Minlie, Tang, Jian, Tang, Jie
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the construction of BMs and the BM application in many fields. At present,
Externí odkaz:
http://arxiv.org/abs/2203.14101
Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machine learning approaches, especially with deep generative models. Inspir
Externí odkaz:
http://arxiv.org/abs/2203.02923
Autor:
Zhu, Zhaocheng, Shi, Chence, Zhang, Zuobai, Liu, Shengchao, Xu, Minghao, Yuan, Xinyu, Zhang, Yangtian, Chen, Junkun, Cai, Huiyu, Lu, Jiarui, Ma, Chang, Liu, Runcheng, Xhonneux, Louis-Pascal, Qu, Meng, Tang, Jian
Machine learning has huge potential to revolutionize the field of drug discovery and is attracting increasing attention in recent years. However, lacking domain knowledge (e.g., which tasks to work on), standard benchmarks and data preprocessing pipe
Externí odkaz:
http://arxiv.org/abs/2202.08320
Reliably predicting the products of chemical reactions presents a fundamental challenge in synthetic chemistry. Existing machine learning approaches typically produce a reaction product by sequentially forming its subparts or intermediate molecules.
Externí odkaz:
http://arxiv.org/abs/2106.07801
Autor:
Xu, Minkai, Wang, Wujie, Luo, Shitong, Shi, Chence, Bengio, Yoshua, Gomez-Bombarelli, Rafael, Tang, Jian
Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing approaches are usually divided into two steps by first predicting the distances between atoms and then generating
Externí odkaz:
http://arxiv.org/abs/2105.07246