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2020 – today
- 2023
[i12]Jonathan P. Mailoa, Zhaofeng Ye, Jiezhong Qiu, Chang-Yu Hsieh, Shengyu Zhang:
Protein-Ligand Complex Generator & Drug Screening via Tiered Tensor Transform. CoRR abs/2301.00984 (2023)- 2022
- [j18]Dejun Jiang, Huiyong Sun
, Jike Wang, Chang-Yu Hsieh, Yuquan Li, Zhenxing Wu, Dong-Sheng Cao, Jian Wu, Tingjun Hou:
Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning. Briefings Bioinform. 23(2) (2022) - [j17]Zhenxing Wu, Dejun Jiang, Jike Wang, Xujun Zhang, Hongyan Du, Lurong Pan, Chang-Yu Hsieh, Dongsheng Cao, Tingjun Hou:
Knowledge-based BERT: a method to extract molecular features like computational chemists. Briefings Bioinform. 23(3) (2022) - [j16]Zi-Yi Yang, Zhaofeng Ye, Yijia Xiao, Chang-Yu Hsieh, Sheng-Yu Zhang:
SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance. Briefings Bioinform. 23(3) (2022) - [j15]Mingyang Wang, Jike Wang, Gaoqi Weng, Yu Kang, Peichen Pan, Dan Li, Yafeng Deng, Honglin Li, Chang-Yu Hsieh, Tingjun Hou:
ReMODE: a deep learning-based web server for target-specific drug design. J. Cheminformatics 14(1): 84 (2022) - [j14]Yuwei Yang, Zhenxing Wu, Xiaojun Yao, Yu Kang, Tingjun Hou, Chang-Yu Hsieh, Huanxiang Liu:
Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation. J. Chem. Inf. Model. 62(13): 3191-3199 (2022) - [j13]Jialu Wu, Junmei Wang, Zhenxing Wu, Shengyu Zhang, Yafeng Deng, Yu Kang, Dong-Sheng Cao, Chang-Yu Hsieh, Tingjun Hou:
ALipSol: An Attention-Driven Mixture-of-Experts Model for Lipophilicity and Solubility Prediction. J. Chem. Inf. Model. 62(23): 5975-5987 (2022) - [j12]Yu-Qin Chen, Yu Chen, Chee-Kong Lee, Shengyu Zhang, Chang-Yu Hsieh:
Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks. Nat. Mach. Intell. 4(3): 269-278 (2022) - [j11]Yuquan Li, Chang-Yu Hsieh, Ruiqiang Lu, Xiaoqing Gong, Xiaorui Wang, Pengyong Li, Shuo Liu, Yanan Tian, Dejun Jiang, Jiaxian Yan, Qifeng Bai, Huanxiang Liu, Shengyu Zhang, Xiaojun Yao:
An adaptive graph learning method for automated molecular interactions and properties predictions. Nat. Mach. Intell. 4(7): 645-651 (2022) - [c2]Yue Wan, Chang-Yu Hsieh, Ben Liao, Shengyu Zhang:
Retroformer: Pushing the Limits of End-to-end Retrosynthesis Transformer. ICML 2022: 22475-22490 - [i11]Yue Wan, Benben Liao, Chang-Yu Hsieh, Shengyu Zhang:
Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer. CoRR abs/2201.12475 (2022) - [i10]Lixue Cheng, Ziyi Yang, Benben Liao, Chang-Yu Hsieh, Shengyu Zhang:
ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution. CoRR abs/2205.09548 (2022) - 2021
- [j10]Pengyong Li, Yuquan Li, Chang-Yu Hsieh, Shengyu Zhang, Xianggen Liu, Huanxiang Liu, Sen Song, Xiaojun Yao:
TrimNet: learning molecular representation from triplet messages for biomedicine. Briefings Bioinform. 22(4) (2021) - [j9]Zhenxing Wu, Dejun Jiang, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Dong-Sheng Cao, Tingjun Hou:
Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method. Briefings Bioinform. 22(5) (2021) - [j8]Xiao-Chen Zhang, Chengkun Wu, Zhi-Jiang Yang, Zhen-Xing Wu, Jia-Cai Yi, Chang-Yu Hsieh, Tingjun Hou, Dong-Sheng Cao:
MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction. Briefings Bioinform. 22(6) (2021) - [j7]Dejun Jiang, Zhenxing Wu, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Zhe Wang, Chao Shen, Dong-Sheng Cao, Jian Wu, Tingjun Hou:
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models. J. Cheminformatics 13(1): 12 (2021) - [j6]Shi-Xin Zhang, Chang-Yu Hsieh, Shengyu Zhang, Hong Yao:
Neural predictor based quantum architecture search. Mach. Learn. Sci. Technol. 2(4): 45027 (2021) - [j5]Guo-Li Xiong, Zhenxing Wu, Jia-Cai Yi, Li Fu, Zhi-Jiang Yang, Chang-Yu Hsieh, Mingzhu Yin, Xiangxiang Zeng, Chengkun Wu, Ai-Ping Lu, Xiang Chen, Tingjun Hou, Dong-Sheng Cao:
ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 49(Webserver-Issue): 5-14 (2021) - [j4]Jike Wang, Chang-Yu Hsieh, Mingyang Wang, Xiaorui Wang, Zhenxing Wu, Dejun Jiang, Benben Liao, Xujun Zhang, Bo Yang, Qiaojun He, Dongsheng Cao, Xi Chen, Tingjun Hou:
Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning. Nat. Mach. Intell. 3(10): 914-922 (2021) - [c1]Junsheng Kong, Weizhao Li, Zeyi Liu, Ben Liao, Jiezhong Qiu, Chang-Yu Hsieh, Yi Cai, Shengyu Zhang:
Fast Extraction of Word Embedding from Q-contexts. CIKM 2021: 873-882 - [i9]Yijia Xiao, Jiezhong Qiu, Ziang Li, Chang-Yu Hsieh, Jie Tang:
Modeling Protein Using Large-scale Pretrain Language Model. CoRR abs/2108.07435 (2021) - [i8]Junsheng Kong, Weizhao Li, Zeyi Liu, Ben Liao, Jiezhong Qiu, Chang-Yu Hsieh, Yi Cai, Shengyu Zhang:
Fast Extraction of Word Embedding from Q-contexts. CoRR abs/2109.07084 (2021) - [i7]Ziyi Yang, Zhaofeng Ye, Yijia Xiao, Chang-Yu Hsieh:
SPLDExtraTrees: Robust machine learning approach for predicting kinase inhibitor resistance. CoRR abs/2111.08008 (2021) - [i6]Junsheng Kong, Weizhao Li, Ben Liao, Jiezhong Qiu, Chang-Yu Hsieh, Yi Cai, Jinhui Zhu, Shengyu Zhang:
Learning Large-scale Network Embedding from Representative Subgraph. CoRR abs/2112.01442 (2021) - 2020
- [j3]Jonathan Allcock, Chang-Yu Hsieh:
A quantum extension of SVM-perf for training nonlinear SVMs in almost linear time. Quantum 4: 342 (2020) - [i5]Jonathan Allcock, Chang-Yu Hsieh:
Quantum algorithm for training nonlinear SVMs in almost linear time. CoRR abs/2006.10299 (2020)
2010 – 2019
- 2019
- [i4]Guangyong Chen, Pengfei Chen, Yujun Shi, Chang-Yu Hsieh, Benben Liao, Shengyu Zhang:
Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks. CoRR abs/1905.05928 (2019) - [i3]Pengfei Chen, Weiwen Liu, Chang-Yu Hsieh, Guangyong Chen, Shengyu Zhang:
Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization. CoRR abs/1906.05488 (2019) - [i2]Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard S. Zemel, Shengyu Zhang:
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models. CoRR abs/1906.09427 (2019) - 2018
- [i1]Jonathan Allcock, Chang-Yu Hsieh, Iordanis Kerenidis, Shengyu Zhang:
Quantum algorithms for feedforward neural networks. CoRR abs/1812.03089 (2018) - 2017
- [j2]Chen-Fu Chiang, Chang-Yu Hsieh:
Resonant transition-based quantum computation. Quantum Inf. Process. 16(5): 120 (2017) - 2014
- [j1]Chang-Yu Hsieh, Raymond Kapral:
Correlation Functions in Open Quantum-Classical Systems. Entropy 16(1): 200-220 (2014)
Coauthor Index
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