no code implementations • 12 Apr 2023 • Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen
Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.
1 code implementation • 8 Oct 2022 • Zaixi Zhang, Qi Liu, Qingyong Hu, Chee-Kong Lee
The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision.
no code implementations • 16 Sep 2022 • Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chee-Kong Lee, Enhong Chen
One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns.
no code implementations • 3 Mar 2022 • HUI ZHANG, Jonathan Wei Zhong Lau, Lingxiao Wan, Liang Shi, Hong Cai, Xianshu Luo, Patrick Lo, Chee-Kong Lee, Leong-Chuan Kwek, Ai Qun Liu
Machine learning methods have revolutionized the discovery process of new molecules and materials.
1 code implementation • NeurIPS 2021 • Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Chee-Kong Lee
To bridge this gap, we propose Motif-based Graph Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif generation framework for GNNs.
1 code implementation • 22 Jun 2019 • Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard Zemel, Shengyu Zhang
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science.