Author

Xiaoqin Tan

ByteDance (China)Primary ORCID profile (0000-0001-7929-1178 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Same name, identified by ORCID

Xiaoqin TaniD1 paper · Army Medical University

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:drug response / drug discovery· 1 Research Focus:perturbation response prediction· 1 Assay:proteomics· 1 Research Focus:sequence-to-function modeling· 1 Research Focus:variant effect prediction· 1 Organism:virus· 1

Frequent Co-Authors

Chunhui WuiD· 1 shared Cui CheniD· 1 shared Feipu YangiD· 1 shared Feisheng ZhongiD· 1 shared He YangiD· 1 shared Hualiang JiangiD· 1 shared Jingshan SheniD· 1 shared Mingyue ZhengiD· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Chunhui WuORCID

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2 papers

Recent·Most cited

Co-evolution integrated deep learning framework for variants generation and fitness prediction

EVPMM is a deep learning framework that predicts vital mutation sites and fitness of viral proteins by integrating co-evolution profiles, outperforming existing methods.

bioRxiv · Jan 28, 2023 · PreprintBioinformatics

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning

Xiaoqin Tan, Xiangrui Jiang, He Yang, Feisheng Zhong, Xutong Li, Zhaoping Xiong, Zhaojun Li, Xiaohong Liu, Cui Chen, Qingjie Zhao, Yuanchao Xie, Feipu Yang, Chunhui Wu, Jingshan Shen, Mingyue Zheng, , Hualiang Jiang

This paper develops a deep learning system to design multitarget antipsychotic drugs, generating novel compounds with activity against multiple receptors and demonstrating efficacy in animal models.

European Journal of Medicinal Chemistry · Jul 11, 2020Peer-reviewedBioinformatics