no code implementations • NeurIPS 2023 • Weitao Du, Jiujiu Chen, Xuecang Zhang, ZhiMing Ma, Shengchao Liu
The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery.
no code implementations • Mathematics 2022 • Yuyan Ni, Yanyan Lan, Ao Liu, ZhiMing Ma
Comparing IB and DIB on these terms, we prove that DIB's SG bound is tighter than IB's while DIB's RD is larger than IB's.
no code implementations • 11 Oct 2023 • Bowen Gao, Yinjun Jia, Yuanle Mo, Yuyan Ni, WeiYing Ma, ZhiMing Ma, Yanyan Lan
Pocket representations play a vital role in various biomedical applications, such as druggability estimation, ligand affinity prediction, and de novo drug design.
1 code implementation • NeurIPS 2023 • Shengchao Liu, Weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, ZhiMing Ma, Omar Yaghi, Anima Anandkumar, Christian Borgs, Jennifer Chayes, Hongyu Guo, Jian Tang
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.
no code implementations • 5 Jun 2023 • Yimeng Chen, Tianyang Hu, Fengwei Zhou, Zhenguo Li, ZhiMing Ma
The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models.
1 code implementation • 28 May 2023 • Shengchao Liu, Weitao Du, ZhiMing Ma, Hongyu Guo, Jian Tang
Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules.
no code implementations • 18 Oct 2022 • Yuancheng Sun, Yimeng Chen, Weizhi Ma, Wenhao Huang, Kang Liu, ZhiMing Ma, Wei-Ying Ma, Yanyan Lan
In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively.
1 code implementation • 29 Jun 2022 • Yimeng Chen, Ruibin Xiong, ZhiMing Ma, Yanyan Lan
Motivated by this, we design a new group invariant learning method, which constructs groups with statistical independence tests, and reweights samples by group label proportion to meet the criteria.