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2020 – today
- 2024
[j32]Yue Guo
, Haitao Hu, Wenbo Chen, Hao Yin, Jian Wu, Chang-Yu Hsieh, Qiaojun He, Ji Cao:
SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction. Briefings Bioinform. 25(2) (2024)- [j31]Ruofan Jin, Qing Ye, Jike Wang, Zheng Cao, Dejun Jiang, Tianyue Wang, Yu Kang, Wanting Xu, Chang-Yu Hsieh, Tingjun Hou:
AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences. Briefings Bioinform. 25(4) (2024) - [j30]Wenjia Qian, Xiaorui Wang, Yu Kang, Peichen Pan, Tingjun Hou, Chang-Yu Hsieh:
A general model for predicting enzyme functions based on enzymatic reactions. J. Cheminformatics 16(1): 38 (2024) - [j29]Mingyang Wang, Zhengjian Wu, Jike Wang, Gaoqi Weng, Yu Kang, Peichen Pan, Dan Li, Yafeng Deng, Xiaojun Yao, Zhitong Bing, Chang-Yu Hsieh, Tingjun Hou:
Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space. J. Chem. Inf. Model. 64(4): 1213-1228 (2024) - [j28]Chao Shen, Jianfei Song, Chang-Yu Hsieh, Dong-Sheng Cao, Yu Kang, Wenling Ye, Zhenxing Wu, Jike Wang, Odin Zhang, Xujun Zhang, Hao Zeng, Heng Cai, Yu Chen, Linkang Chen, Hao Luo, Xinda Zhao, Tianye Jian, Tong Chen, Dejun Jiang, Mingyang Wang, Qing Ye, Jialu Wu, Hongyan Du, Hui Shi, Yafeng Deng, Tingjun Hou:
DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery. J. Chem. Inf. Model. 64(14): 5381-5391 (2024) - [j27]Bojia Duan, Xin Sun, Chang-Yu Hsieh:
Parallelized variational quantum classifier with shallow QRAM circuit. Quantum Inf. Process. 23(3): 92 (2024) - [i25]Yiheng Zhu, Zitai Kong, Jialu Wu, Weize Liu, Yuqiang Han, Mingze Yin, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou:
Generative AI for Controllable Protein Sequence Design: A Survey. CoRR abs/2402.10516 (2024) - [i24]Odin Zhang, Yufei Huang, Shichen Cheng, Mengyao Yu, Xujun Zhang, Haitao Lin, Yundian Zeng, Mingyang Wang, Zhenxing Wu, Huifeng Zhao, Zaixi Zhang, Chenqing Hua, Yu Kang, Sunliang Cui, Peichen Pan, Chang-Yu Hsieh, Tingjun Hou:
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation. CoRR abs/2404.00014 (2024) - [i23]Lijun Liu, Jiali Yang, Jianfei Song, Xinglin Yang, Lele Niu, Zeqi Cai, Hui Shi, Tingjun Hou, Chang-Yu Hsieh, Weiran Shen, Yafeng Deng:
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation. CoRR abs/2404.10573 (2024) - [i22]Odin Zhang, Haitao Lin, Hui Zhang, Huifeng Zhao, Yufei Huang, Yuansheng Huang, Dejun Jiang, Chang-Yu Hsieh, Peichen Pan, Tingjun Hou:
Deep Lead Optimization: Leveraging Generative AI for Structural Modification. CoRR abs/2404.19230 (2024) - [i21]Jiebin Fang, Churu Mao, Yuchen Zhu, Xiaoming Chen, Chang-Yu Hsieh, Zhongjun Ma:
Human-level molecular optimization driven by mol-gene evolution. CoRR abs/2406.12910 (2024) - [i20]Jike Wang, Rui Qin, Mingyang Wang, Meijing Fang, Yangyang Zhang, Yuchen Zhu, Qun Su, Qiaolin Gou, Chao Shen, Odin Zhang, Zhenxing Wu, Dejun Jiang, Xujun Zhang, Huifeng Zhao, Xiaozhe Wan, Zhourui Wu, Liwei Liu, Yu Kang, Chang-Yu Hsieh, Tingjun Hou:
Token-Mol 1.0: Tokenized drug design with large language model. CoRR abs/2407.07930 (2024) - [i19]Mingze Yin, Hanjing Zhou, Yiheng Zhu, Miao Lin, Yixuan Wu, Jialu Wu, Hongxia Xu, Chang-Yu Hsieh, Tingjun Hou, Jintai Chen, Jian Wu:
Multi-Modal CLIP-Informed Protein Editing. CoRR abs/2407.19296 (2024) - [i18]Chunbin Gu, Mutian He, Hanqun Cao, Guangyong Chen, Chang-Yu Hsieh, Pheng-Ann Heng:
Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries. CoRR abs/2409.05916 (2024) - [i17]Hanqun Cao, Chunbin Gu, Mutian He, Ning Ma, Chang-Yu Hsieh, Pheng-Ann Heng:
DEL-Ranking: Ranking-Correction Denoising Framework for Elucidating Molecular Affinities in DNA-Encoded Libraries. CoRR abs/2410.14946 (2024) - 2023
- [j26]Yanqing Duan, Li Fu, Xiao-Chen Zhang, Teng-Zhi Long, Yuan-Hang He, Zhao-Qian Liu, Ai-Ping Lu, Yafeng Deng, Chang-Yu Hsieh, Tingjun Hou, Dong-Sheng Cao:
Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data. J. Chem. Inf. Model. 63(8): 2345-2359 (2023) - [j25]Yuwei Yang, Chang-Yu Hsieh, Yu Kang, Tingjun Hou, Huanxiang Liu, Xiaojun Yao:
Deep Generation Model Guided by the Docking Score for Active Molecular Design. J. Chem. Inf. Model. 63(10): 2983-2991 (2023) - [j24]Jike Wang, Yundian Zeng, Huiyong Sun, Junmei Wang, Xiaorui Wang, Ruofan Jin, Mingyang Wang, Xujun Zhang, Dong-Sheng Cao, Xi Chen, Chang-Yu Hsieh, Tingjun Hou:
Molecular Generation with Reduced Labeling through Constraint Architecture. J. Chem. Inf. Model. 63(11): 3319-3327 (2023) - [j23]Zhenxing Wu, Jihong Chen, Yitong Li, Yafeng Deng, Haitao Zhao, Chang-Yu Hsieh, Tingjun Hou:
From Black Boxes to Actionable Insights: A Perspective on Explainable Artificial Intelligence for Scientific Discovery. J. Chem. Inf. Model. 63(24): 7617-7627 (2023) - [j22]Odin Zhang, Jintu Zhang, Jieyu Jin, Xujun Zhang, Renling Hu, Chao Shen, Hanqun Cao, Hongyan Du, Yu Kang, Yafeng Deng, Furui Liu, Guangyong Chen, Chang-Yu Hsieh, Tingjun Hou:
ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling. Nat. Mac. Intell. 5(9): 1020-1030 (2023) - [j21]Xujun Zhang, Odin Zhang, Chao Shen, Wanglin Qu, Shicheng Chen, Hanqun Cao, Yu Kang, Zhe Wang, Ercheng Wang, Jintu Zhang, Yafeng Deng, Furui Liu, Tianyue Wang, Hongyan Du, Langcheng Wang, Peichen Pan, Guangyong Chen, Chang-Yu Hsieh, Tingjun Hou:
Efficient and accurate large library ligand docking with KarmaDock. Nat. Comput. Sci. 3(9): 789-804 (2023) - [j20]Odin Zhang, Tianyue Wang, Gaoqi Weng, Dejun Jiang, Ning Wang, Xiaorui Wang, Huifeng Zhao, Jialu Wu, Ercheng Wang, Guangyong Chen, Yafeng Deng, Peichen Pan, Yu Kang, Chang-Yu Hsieh, Tingjun Hou:
Learning on topological surface and geometric structure for 3D molecular generation. Nat. Comput. Sci. 3(10): 849-859 (2023) - [j19]Shi-Xin Zhang, Jonathan Allcock, Zhou-Quan Wan, Shuo Liu, Jiace Sun, Hao Yu, Xing-Han Yang, Jiezhong Qiu, Zhaofeng Ye, Yu-Qin Chen, Chee-Kong Lee, Yicong Zheng, Shao-Kai Jian, Hong Yao, Chang-Yu Hsieh, Shengyu Zhang:
TensorCircuit: a Quantum Software Framework for the NISQ Era. Quantum 7: 912 (2023) - [c4]Yiheng Zhu, Zhenqiu Ouyang, Ben Liao, Jialu Wu, Yixuan Wu, Chang-Yu Hsieh, Tingjun Hou, Jian Wu:
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation. IJCAI 2023: 5002-5010 - [c3]Yiheng Zhu, Jialu Wu, Chaowen Hu, Jiahuan Yan, Chang-Yu Hsieh, Tingjun Hou, Jian Wu:
Sample-efficient Multi-objective Molecular Optimization with GFlowNets. NeurIPS 2023 - [i16]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) - [i15]Yiheng Zhu, Jialu Wu, Chaowen Hu, Jiahuan Yan, Chang-Yu Hsieh, Tingjun Hou, Jian Wu:
Sample-efficient Multi-objective Molecular Optimization with GFlowNets. CoRR abs/2302.04040 (2023) - [i14]Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen:
An Equivariant Generative Framework for Molecular Graph-Structure Co-Design. CoRR abs/2304.12436 (2023) - [i13]Yiheng Zhu, Zhenqiu Ouyang, Ben Liao, Jialu Wu, Yixuan Wu, Chang-Yu Hsieh, Tingjun Hou, Jian Wu:
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation. CoRR abs/2305.08457 (2023) - [i12]Yue Wan, Jialu Wu, Tingjun Hou, Chang-Yu Hsieh, Xiaowei Jia:
From molecules to scaffolds to functional groups: building context-dependent molecular representation via multi-channel learning. CoRR abs/2311.02798 (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, Jiacai 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, Jiacai 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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