Z
Zhaoping Xiong
Researcher at ShanghaiTech University
Publications - 21
Citations - 935
Zhaoping Xiong is an academic researcher from ShanghaiTech University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 8, co-authored 16 publications receiving 360 citations. Previous affiliations of Zhaoping Xiong include Chinese Academy of Sciences & Huawei.
Papers
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Journal ArticleDOI
Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.
Zhaoping Xiong,Zhaoping Xiong,Dingyan Wang,Xiaohong Liu,Xiaohong Liu,Feisheng Zhong,Xiaozhe Wan,Xutong Li,Zhaojun Li,Xiaomin Luo,Kaixian Chen,Kaixian Chen,Hualiang Jiang,Hualiang Jiang,Mingyue Zheng +14 more
TL;DR: A new graph neural network architecture called Attentive FP for molecular representation that uses a graph attention mechanism to learn from relevant drug discovery datasets and achieves state-of-the-art predictive performances on a variety of datasets and that what it learns is interpretable.
Journal ArticleDOI
In silico ADME/T modelling for rational drug design
Yulan Wang,Jing Xing,Yuan Xu,Nannan Zhou,Jianlong Peng,Zhaoping Xiong,Xian Liu,Xiaomin Luo,Cheng Luo,Kaixian Chen,Mingyue Zheng,Hualiang Jiang +11 more
TL;DR: The development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models are introduced.
Journal ArticleDOI
Artificial intelligence in drug design.
Feisheng Zhong,Jing Xing,Xutong Li,Xiaohong Liu,Xiaohong Liu,Zunyun Fu,Zhaoping Xiong,Zhaoping Xiong,Dong Lu,Xiaolong Wu,Jihui Zhao,Xiaoqin Tan,Fei Li,Fei Li,Xiaomin Luo,Zhaojun Li,Kaixian Chen,Kaixian Chen,Mingyue Zheng,Hualiang Jiang,Hualiang Jiang +20 more
TL;DR: Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.
Journal ArticleDOI
Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation
Xutong Li,Zhaojun Li,Xiaolong Wu,Xiaolong Wu,Zhaoping Xiong,Zhaoping Xiong,Tianbiao Yang,Zunyun Fu,Xiaohong Liu,Xiaohong Liu,Xiaoqin Tan,Feisheng Zhong,Xiaozhe Wan,Dingyan Wang,Xiaoyu Ding,Ruirui Yang,Ruirui Yang,Hui Hou,Hui Hou,Chunpu Li,Hong Liu,Kaixian Chen,Kaixian Chen,Hualiang Jiang,Hualiang Jiang,Mingyue Zheng +25 more
TL;DR: A virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm is presented to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning.
Journal ArticleDOI
Generative Models for De Novo Drug Design.
Xiaochu Tong,Xiaohong Liu,Xiaoqin Tan,Xutong Li,Jiaxin Jiang,Zhaoping Xiong,Tingyang Xu,Hualiang Jiang,Nan Qiao,Mingyue Zheng +9 more
TL;DR: In this paper, the authors summarize the applications of generative models to drug design, including generating various compounds to expand the compound library and designing compounds with specific properties, and also list a few publicly available molecular design tools based on Generative models which can be used directly to generate molecules.