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成果及论文

#共同第一作者，*通讯作者

代表性论文

  1. Zhenxing Wu, Odin Zhang, et al. Leveraging language model for advanced multiproperty molecular optimization via prompt engineering. Nature Machine Intelligence, 2024: 1-11. (中科院TOP，中科院1区，5年IF: 26.4)
  2. Zhenxing Wu, Jike Wang, et al. Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking. Nature Communications, 2023, 14(1): 2585. (中科院TOP，中科院1区，5年IF: 16.1)
  3. Zhenxing Wu, Dejun Jiang, et al. Mining toxicity information from large amounts of toxicity data. Journal of Medicinal Chemistry, 2021, 64, 6924-6936. (中科院TOP，中科院1区，5年IF: 7.1)
  4. Xiong, G.#, Zhenxing Wu#, et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research, 2021, 49(W1), W5-W14. (中科院TOP，中科院2区，5年IF: 16.1，ESI高被引论文)
  5. Zhenxing Wu, Dejun Jiang, et al. Knowledge-based BERT: a method to extract molecular features like computational chemists. Briefings in Bioinformatics, 2022, 23, bbac131. (中科院TOP，中科院1区，5年IF: 7.9)
  6. Zhenxing Wu, Tailong Lei, et al. ADMET evaluation in drug discovery. 19. Reliable prediction of human cytochrome P450 inhibition using artificial intelligence approaches. Journal of Chemical Information and Modeling, 2019, 59, 4587-4601. (中科院TOP，中科院2区，5年IF: 5.9)
  7. Zhenxing Wu #, Dejun Jiang#, et al. Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method. Briefings in Bioinformatics, 2021, 22, bbab112. (中科院TOP，中科院1区，5年IF: 7.9)
  8. Zhenxing Wu, Jihong Chen, et al. From black boxes to actionable insights: a perspective on explainable artificial intelligence for scientific discovery. Journal of Chemical Information and Modeling, 2023, 63(24): 7617-7627. (中科院TOP，中科院2区，5年IF: 5.9)
  9. Zhenxing Wu, Minfeng Zhu, et al. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets. Briefings in Bioinformatics, 2021, 22, bbaa321. (中科院TOP，中科院1区，5年IF: 7.9)
  10. Dejun Jiang #, Zhenxing Wu#, et al. Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models, Journal of Cheminformatics, 2021, 13, 1-23. (中科院2区，5年IF: 9.3)

其他论文

  1. Mingyang Wang, Shuai Li, Jike Wang, Odin Zhang, Hongyan Du, Dejun Jiang, Zhenxing Wu, Yafeng Deng, Yu Kang, Peichen Pan, Dan Li, Xiaorui Wang, Tingjun Hou*, Chang-Yu Hsieh*, ClickGen: Directed Exploration of Synthesizable Chemical Space Leading to the Rapid Synthesis of Novel and Active Lead Compounds via Modular Reactions and Reinforcement Learning, Nature Communications, 2024, accepted.
  2. Xiaorui Wang, Xiaodan Yin, Dejun Jiang, Huifeng Zhao, Zhenxing Wu, Odin Zhang, Jike Wang, Yuquan Li, Yafeng Deng, Huanxiang Liu, Pei Luo, Yuqiang Han, Tingjun Hou*, Xiaojun Yao*, Chang-Yu Hsieh*, Multi-Modal Deep Learning Enables Ultrafast and Accurate Annotation of Enzymatic Active Sites, Nature Communications, 2024, 15, 7348.
  3. Chao Shen, Jianfei Song, Chang-Yu Hsieh, Dongsheng 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, Journal of Chemical Information and Modeling, 2024, 64, 5381-5391.
  4. Li Fu, Shaohua Shi, Jiacai Yi, Ningning Wang, Yuanhang He, Zhenxing Wu, Jinfu Peng, Youchao Deng, Wenxuan Wang, Chengkun Wu, Aiping Lyu, Xiangxiang Zeng, Wentao Zhao, Tingjun Hou*, Dongsheng Cao*, ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support, Nucleic Acids Research, 2024, 52(W1), W422-W431.
  5. Dejun Jiang, Hongyan Du, Huifeng Zhao, Yafeng Deng, Zhenxing Wu, Jike Wang, Yundian Zeng, Haotian Zhang, Xiaorui Wang, Ercheng Wang, Tingjun Hou*, Chang-Yu Hsieh*, Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes, Physical Chemistry Chemical Physics, 2024, 26, 10323-10335.
  6. Xiaodan Yin, Chang-Yu Hsieh*, Xiaorui Wang, Zhenxing Wu, Qing Ye, Yafeng Deng, Hongming Chen, Pei Luo, Huanxiang Liu, Tingjun Hou*, Xiaojun Yao*, Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning, Research, 2024, 7, 0292.
  7. Hongyan Du, Dejun Jiang, Odin Zhang, Zhenxing Wu, Junbo Gao, Xujun Zhang, Xiaorui Wang, Yafeng Deng, Yu Kang, Dan Li, Peichen Pan*, Chang-Yu Hsieh*, Tingjun Hou*, A Flexible Data-Free Framework for Structure-Based De Novo Drug Design with Reinforcement Learning, Chemical Science, 2023, 14, 12166-12181.
  8. Xiaorui Wang, Chang-Yu Hsieh*, Xiaodan Yin, Jike Wang, Yuquan Li, Yafeng Deng, Dejun Jiang, Zhenxing Wu, Hongyan Du, Hongming Chen, Yun Li, Huanxiang Liu, Yuwei Wang, Pei Luo, Tingjun Hou*, Xiaojun Yao*, Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center, Research, 2023, 6, 0231.
  9. Dejun Jiang, Huifeng Zhao, Hongyan Du, Yafeng Deng, Zhenxing Wu, Jike Wang, Yundian Zeng, Haotian Zhang, Xiaorui Wang, Jian Wu*, Chang-Yu Hsieh*, Tingjun Hou*, How Good Are Current Docking Programs at Nucleic Acid-Ligand Docking? A Comprehensive Evaluation, Journal of Chemical Theory and Computation, 2023, 66, 10808-10823.
  10. Dejun Jiang, Zhaofeng Ye, Chang-Yu Hsieh, Zhiyi Yang, Xujun Zhang, Yu Kang, Hongyan Du, Zhenxing Wu, Jike Wang, Yundian Zeng, Haotian Zhang, Xiaorui Wang, Mingyang Wang, Xiaojun Yao, Shengyu Zhang*, Jian Wu*, Tingjun Hou*, MetalProGNet: A Structure-based Deep Graph Model for Metalloprotein-Ligand Interaction Predictions, Chemical Science, 2023, 14, 2054-2069.
  11. Dong Wang, Zhenxing Wu, Chao Shen, Lingjie Bao, Hao Luo, Zhe Wang, Hucheng Yao, Dexin Kong*, Cheng Luo*, Tingjun Hou*, Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors, Briefings in Bioinformatics, 2023, 1, bbac592.
  12. Jialu Wu, Yue Wan, Zhenxing Wu, Shengyu Zhang, Dongsheng Cao*, Chang-Yu Hsieh*, Tingjun Hou*, MF-SuP-pKa: Multi-fidelity modeling with subgraph pooling mechanism for pKa prediction, Acta Pharmaceutica Sinica B, 2023, 13, 2572-2584.
  13. Lingjie Bao, Zhe Wang, Zhenxing Wu, Hao Luo, Jiahui Yu, Yu Kang*, Dongsheng Cao*, Tingjun Hou*, Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach, Acta Pharmaceutica Sinica B, 2023, 13, 54-67.
  14. Jialu Wu, Junmei Wang, Zhenxing Wu, Shengyu Zhang, Yafeng Deng, Yu Kang, Dongsheng Cao*, Chang-Yu Hsieh*, Tingjun Hou*, ALipSol: An Attention-Driven Mixture-of-Experts Model for Lipophilicity and Solubility Prediction, Journal of Chemical Information and Modeling, 2022, 62, 5975-5987.
  15. Jike Wang, Xiaorui Wang, Huiyong Sun, Mingyang Wang, Yundian Zeng, Dejun Jiang, Zhenxing Wu, Zeyi Liu, Ben Liao, Xiaojun Yao, Chang-Yu Hsieh*, Dongsheng Cao*, Xi Chen*, Tingjun Hou*, ChemistGA: A Chemical Synthesizable Accessible Molecular Generation Algorithm for Real-World Drug Discovery, Journal of Medicinal Chemistry, 2022, 65, 12482-12496.
  16. Hongyan Du, Dejun Jiang, Junbo Gao, Xujun Zhang, Lingxiao Jiang, Yundian Zeng, Zhenxing Wu, Chao Shen, Lei Xu, Dongsheng Cao*, Tingjun Hou*, Peichen Pan*, Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network, Research, 2022, 9873564.
  17. 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, Journal of Chemical Information and Modeling, 2022, 62, 3191-3199.
  18. Xujun Zhang, Chao Shen, Ben Liao, Dejun Jiang, Jike Wang, Zhenxing Wu, Hongyan Du, Tianyue Wang, Wenbo Huo, Lei Xu, Dongsheng Cao*, Chang-Yu Hsieh*, Tingjun Hou*, TocoDecoy: A New Approach to Design Unbiased Datasets for Training and Benchmarking Machine-Learning Scoring Functions, Journal of Medicinal Chemistry, 2022, 65, 7918-7932.
  19. Dejun Jiang, Huiyong Sun, Jike Wang, Changyu Hsieh, Yuquan Li, Zhenxing Wu, Dongsheng Cao*, Jian Wu*, Tingjun Hou*, Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning, Briefings in Bioinformatics, 2022, 23, bbab597.
  20. Dejun Jiang, Chang-Yu Hsieh, Zhenxing Wu, Yu Kang, Jike Wang, Ercheng Wang, Ben Liao, Chao Shen, Lei Xu, Jian Wu*, Dongsheng Cao*, Tingjun Hou*, InteractionGraphNet: a novel and efficient deep graph representation learning framework for accurate protein-ligand interaction predictions, Journal of Medicinal Chemistry, 2021, 64, 18209-18232.
  21. 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, Nature Machine Intelligence, 2021, 3, 914-922.
  22. Jike Wang, Huiyong Sun, Jiawen Chen, Dejun Jiang, Zhe Wang, Zhenxing Wu, Xi Chen*, Dongsheng Cao*, Tingjun Hou*, DeepChargePredictor: A web server for predicting QM-based atomic charges via state-of-the-art machine-learning algorithms, Bioinformatics, 2021, 37, 4255-4257.
  23. Xiaochen Zhang, Chengkun Wu, Zhijiang Yang, Zhenxing Wu, Jiacai Yi, Changyu Hsieh, Tingjun Hou*, Dongsheng Cao*, MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction, Briefings in Bioinformatics, 2021, 22, bbab152.

申请专利

  1. 吴振兴，康玉等，一种分子图输出方法及装置，2023.03.07，CN202310237436.1
  2. 吴振兴; 谢昌谕等，用于证据神经网络模型的协同训练方法及装置，2022.05.20，CN202210558493.5