Xiangxiang Zeng

Research interests

Publications

• 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 · https://doi.org/10.1093/nar/gkae236

• DSN-DDI: an accurate and generalized framework for drug–drug interaction prediction by dual-view representation learning

  Briefings in Bioinformatics · 2023 · https://doi.org/10.1093/bib/bbac597

• A weighted bilinear neural collaborative filtering approach for drug repositioning

  Briefings in Bioinformatics · 2022 · 10.1093/bib/bbab581

• Deep generative molecular design reshapes drug discovery

  Cell Reports Medicine · 2022 · 10.1016/j.xcrm.2022.100794

• Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework

  Nature Machine Intelligence · 2022 · 10.1038/s42256-022-00557-6

• Deep learning for drug repurposing: Methods, databases, and applications

  Wiley Interdisciplinary Reviews Computational Molecular Science · 2022 · https://doi.org/10.1002/wcms.1597

• MUFFIN: multi-scale feature fusion for drug–drug interaction prediction

  Bioinformatics · 2021 · 10.1093/bioinformatics/btab169

• ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

  Nucleic Acids Research · 2021 · https://doi.org/10.1093/nar/gkab255

• Drug repositioning based on the heterogeneous information fusion graph convolutional network

  Briefings in Bioinformatics · 2021 · https://doi.org/10.1093/bib/bbab319

• Toward better drug discovery with knowledge graph

  Current Opinion in Structural Biology · 2021 · 10.1016/j.sbi.2021.09.003

• Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning

  Journal of Proteome Research · 2020 · 10.1021/acs.jproteome.0c00316

• Target identification among known drugs by deep learning from heterogeneous networks

  Chemical Science · 2020 · 10.1039/c9sc04336e

• Application of deep learning methods in biological networks

  Briefings in Bioinformatics · 2020 · 10.1093/bib/bbaa043

• KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction

  · 2020 · 10.24963/ijcai.2020/380

• deepDR: a network-based deep learning approach toin silicodrug repositioning

  Bioinformatics · 2019 · 10.1093/bioinformatics/btz418

• Identifying enhancer–promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism

  Bioinformatics · 2019 · https://doi.org/10.1093/bioinformatics/btz694

• A novel molecular representation with BiGRU neural networks for learning atom

  Briefings in Bioinformatics · 2019 · https://doi.org/10.1093/bib/bbz125

• Prediction of potential disease-associated microRNAs using structural perturbation method

  Bioinformatics · 2018 · 10.1093/bioinformatics/bty112

• Sequence clustering in bioinformatics: an empirical study

  Briefings in Bioinformatics · 2018 · 10.1093/bib/bby090

• A comprehensive overview and evaluation of circular RNA detection tools

  PLoS Computational Biology · 2017 · 10.1371/journal.pcbi.1005420

• Prediction and Validation of Disease Genes Using HeteSim Scores

  IEEE Transactions on Computational Biology and Bioinformatics · 2016 · 10.1109/tcbb.2016.2520947

• Inferring MicroRNA-Disease Associations by Random Walk on a Heterogeneous Network with Multiple Data Sources

  IEEE Transactions on Computational Biology and Bioinformatics · 2016 · 10.1109/tcbb.2016.2550432

• Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks

  Briefings in Bioinformatics · 2015 · 10.1093/bib/bbv033

• Similarity computation strategies in the microRNA-disease network: a survey

  Briefings in Functional Genomics · 2015 · 10.1093/bfgp/elv024

• nDNA-prot: identification of DNA-binding proteins based on unbalanced classification

  BMC Bioinformatics · 2014 · https://doi.org/10.1186/1471-2105-15-298

Current projects

No projects listed.