Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields. - Cheng-Wei Ju; Hanzhi Bai; Bo Li; Rizhang Liu (J. Chem. Inf. Model., 2021)

Improving Subgraph Representation Learning via Multi-View Augmentation. - Yili Shen; Jiaxu Yan; Cheng-Wei Ju; Jun Yi; Zhou Lin; Hui Guan 001 (CoRR, 2022)

Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces. - Siqi Chen; Zhiqiang Wang; Xianqi Deng; Yili Shen; Cheng-Wei Ju; Jun Yi; Lin Xiong; Guo Ling; Dieaa Alhmoud; Hui Guan 001; Zhou Lin (CoRR, 2024)

ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction. - Yuzhi Xu; Xinxin Liu; Wei Xia; Jiankai Ge; Cheng-Wei Ju; Haiping Zhang; John Z. H. Zhang (J. Chem. Inf. Model., 2024)