X
Xiaolong Wu
Researcher at Chinese Academy of Sciences
Publications - 12
Citations - 223
Xiaolong Wu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Deep learning & Virtual screening. The author has an hindex of 4, co-authored 10 publications receiving 99 citations. Previous affiliations of Xiaolong Wu include East China University of Science and Technology.
Papers
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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
KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules.
Zhaojun Li,Xutong Li,Xiaohong Liu,Xiaohong Liu,Zunyun Fu,Zhaoping Xiong,Zhaoping Xiong,Xiaolong Wu,Xiaoqin Tan,Jihui Zhao,Jihui Zhao,Feisheng Zhong,Xiaozhe Wan,Xiaomin Luo,Kaixian Chen,Kaixian Chen,Hualiang Jiang,Hualiang Jiang,Mingyue Zheng +18 more
TL;DR: Overall, KinomeX enables users to create a comprehensive kinome interaction network for designing novel chemical modulators, and is of practical value on exploring the previously less studied or untargeted kinases.
Journal ArticleDOI
Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki–Miyaura cross-coupling reaction
Zunyun Fu,Zunyun Fu,Xutong Li,Zhaohui Wang,Zhaojun Li,Xiaohong Liu,Xiaolong Wu,Jihui Zhao,Xiaoyu Ding,Xiaozhe Wan,Feisheng Zhong,Dingyan Wang,Xiaomin Luo,Kaixian Chen,Kaixian Chen,Hong Liu,Jiang Wang,Hualiang Jiang,Hualiang Jiang,Mingyue Zheng +19 more
TL;DR: In this article, a deep learning model was trained to learn the relationships between the chemical contexts, reaction conditions and product yields based on high-quality existing experimental data, and then extrapolate reasonably to unseen reactions by in silico exploration of accessible reaction space.
Journal ArticleDOI
Drug target inference by mining transcriptional data using a novel graph convolutional network framework
Feisheng Zhong,Xiaolong Wu,Xiaolong Wu,Ruirui Yang,Ruirui Yang,Xutong Li,Dingyan Wang,Zunyun Fu,Zunyun Fu,Xiaohong Liu,Xiaohong Liu,Xiaozhe Wan,Tianbiao Yang,Zisheng Fan,Zisheng Fan,Yinghui Zhang,Xiaomin Luo,Kaixian Chen,Sulin Zhang,Hualiang Jiang,Mingyue Zheng,Mingyue Zheng +21 more
TL;DR: Wang et al. as discussed by the authors proposed a Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles.