World's Best Scientists 2026 revealed!
Xiangxiang Zeng

Xiangxiang Zeng

D-Index & Metrics

Computer Science

D-Index
56
Citations
10549
World Ranking
4133
National Ranking
554

Xiangxiang Zeng publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Xiangxiang Zeng sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 170 publications — 35th percentile

35% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Xiangxiang Zeng D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Xiangxiang Zeng sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Xiangxiang Zeng is affiliated with Hunan University in China and has contributed extensively to research in the intersecting fields of biochemistry, genetics, molecular biology, and computer science. Their work spans various subfields including molecular biology, computational theory and mathematics, materials chemistry, artificial intelligence, and pharmacology.

Their research focuses on several main topics:

  • Computational Drug Discovery Methods
  • Machine Learning in Materials Science
  • Bioinformatics and Genomic Networks
  • Machine Learning in Bioinformatics
  • Protein Structure and Dynamics
  • DNA and Biological Computing
  • Advanced biosensing and bioanalysis techniques

Xiangxiang Zeng has produced a number of recent publications in well-regarded journals. These include:

  • ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties, 2021, Nucleic Acids Research
  • ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support, 2024, Nucleic Acids Research
  • Target identification among known drugs by deep learning from heterogeneous networks, 2020, Chemical Science
  • Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning, 2020, Journal of Proteome Research
  • Deep generative molecular design reshapes drug discovery, 2022, Cell Reports Medicine

The primary venues where Xiangxiang Zeng publishes include:

  • Briefings in Bioinformatics
  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • Journal of Chemical Information and Modeling

Among frequent collaborators are Bosheng Song, Dongsheng Cao, Yuansheng Liu, Xiangrong Liu, and Quan Zou, each contributing to multiple co-authored works, reflecting ongoing research partnerships.

Best Publications

  • deepDR: a network-based deep learning approach to in silico drug repositioning.

    Xiangxiang Zeng;Siyi Zhu;Xiangrong Liu;Yadi Zhou

  • A comprehensive overview and evaluation of circular RNA detection tools.

    Xiangxiang Zeng;Wei Lin;Maozu Guo;Quan Zou

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

    Xiangxiang Zeng;Xuan Zhang;Quan Zou

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

    Xiangxiang Zeng;Siyi Zhu;Weiqiang Lu;Zehui Liu

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

    Yuansheng Liu;Xiangxiang Zeng;Zengyou He;Quan Zou

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

    Xuan Lin;Zhe Quan;Zhi-Jie Wang;Tengfei Ma

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

    Quan Zou;Jinjin Li;Li Song;Xiangxiang Zeng

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

    Xiangxiang Zeng;Xiang Song;Tengfei Ma;Xiaoqin Pan

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

    Xiangxiang Zeng;Li Liu;Linyuan Lü;Quan Zou

  • Sequence clustering in bioinformatics: an empirical study.

    Quan Zou;Quan Zou;Gang Lin;Xingpeng Jiang;Xiangrong Liu

  • Prediction and Validation of Disease Genes Using HeteSim Scores

    Xiangxiang Zeng;Yuanlu Liao;Yuansheng Liu;Quan Zou

  • Toward better drug discovery with knowledge graph.

    Xiangxiang Zeng;Xinqi Tu;Yuansheng Liu;Xiangzheng Fu

  • Application of deep learning methods in biological networks.

    Shuting Jin;Xiangxiang Zeng;Feng Xia;Wei Huang

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

    Li Song;Dapeng Li;Xiangxiang Zeng;Yunfeng Wu

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

    Yujie Chen;Tengfei Ma;Xixi Yang;Jianmin Wang

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

    Zengyan Hong;Xiangxiang Zeng;Leyi Wei;Xiangrong Liu

  • Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy

    Quan Zou;Shixiang Wan;Shixiang Wan;Ying Ju;Jijun Tang;Jijun Tang

  • Deterministic solutions to QSAT and Q3SAT by spiking neural P systems with pre-computed resources

    Tseren-Onolt Ishdorj;Alberto Leporati;Linqiang Pan;Xiangxiang Zeng

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

    Lijun Cai;Changcheng Lu;Junlin Xu;Yajie Meng

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

    Xuan Lin;Zhe Quan;Zhi-Jie Wang;Huang Huang

  • Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods.

    Xiangxiang Zeng;Yue Zhong;Wei Lin;Quan Zou

  • Spiking neural p systems with thresholds

    Xiangxiang Zeng;Xingyi Zhang;Tao Song;Linqiang Pan

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

    Xiangxiang Zeng;Xiang Song;Tengfei Ma;Xiaoqin Pan

Frequent Co-Authors

Quan Zou
Quan Zou University of Electronic Science and Technology of China
Linqiang Pan
Linqiang Pan Huazhong University of Science and Technology
Feixiong Cheng
Feixiong Cheng Case Western Reserve University
Xingyi Zhang
Xingyi Zhang Anhui University
Ruth Nussinov
Ruth Nussinov National Institutes of Health
Stephen C. H. Leung
Stephen C. H. Leung City University of Hong Kong
Linyuan Lü
Linyuan Lü University of Electronic Science and Technology of China
Jijun Tang
Jijun Tang University of South Carolina
Gary G. Yen
Gary G. Yen Oklahoma State University
Bin Luo
Bin Luo Anhui University

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