Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Li, Zhuoxinran"'
Autor:
Liu, Shengchao, Yan, Divin, Du, Weitao, Liu, Weiyang, Li, Zhuoxinran, Guo, Hongyu, Borgs, Christian, Chayes, Jennifer, Anandkumar, Anima
Artificial intelligence models have shown great potential in structure-based drug design, generating ligands with high binding affinities. However, existing models have often overlooked a crucial physical constraint: atoms must maintain a minimum pai
Externí odkaz:
http://arxiv.org/abs/2409.10584
Autor:
Liu, Shengchao, Wang, Chengpeng, Lu, Jiarui, Nie, Weili, Wang, Hanchen, Li, Zhuoxinran, Zhou, Bolei, Tang, Jian
Deep generative models (DGMs) have been widely developed for graph data. However, much less investigation has been carried out on understanding the latent space of such pretrained graph DGMs. These understandings possess the potential to provide cons
Externí odkaz:
http://arxiv.org/abs/2401.17123
Autor:
Liu, Shengchao, Du, Weitao, Li, Yanjing, Li, Zhuoxinran, Bhethanabotla, Vignesh, Rampal, Nakul, Yaghi, Omar, Borgs, Christian, Anandkumar, Anima, Guo, Hongyu, Chayes, Jennifer
In drug discovery, molecular dynamics (MD) simulation for protein-ligand binding provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. There has been a long history of improving the e
Externí odkaz:
http://arxiv.org/abs/2401.15122
Autor:
Liu, Shengchao, Du, Weitao, Li, Yanjing, Li, Zhuoxinran, Zheng, Zhiling, Duan, Chenru, Ma, Zhiming, Yaghi, Omar, Anandkumar, Anima, Borgs, Christian, Chayes, Jennifer, Guo, Hongyu, Tang, Jian
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery. For these scientific probl
Externí odkaz:
http://arxiv.org/abs/2306.09375
Autor:
Liu, Shengchao, Li, Yanjing, Li, Zhuoxinran, Gitter, Anthony, Zhu, Yutao, Lu, Jiarui, Xu, Zhao, Nie, Weili, Ramanathan, Arvind, Xiao, Chaowei, Tang, Jian, Guo, Hongyu, Anandkumar, Anima
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format describing proteins' high-level functionalities. Yet, whether the inco
Externí odkaz:
http://arxiv.org/abs/2302.04611