Shichao Liu

1.5k total citations · 1 hit paper
45 papers, 966 citations indexed

About

Shichao Liu is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Shichao Liu has authored 45 papers receiving a total of 966 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 19 papers in Computational Theory and Mathematics and 9 papers in Materials Chemistry. Recurrent topics in Shichao Liu's work include Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (8 papers) and Machine Learning in Bioinformatics (4 papers). Shichao Liu is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (8 papers) and Machine Learning in Bioinformatics (4 papers). Shichao Liu collaborates with scholars based in China, United States and Australia. Shichao Liu's co-authors include Wen Zhang, Yang Qiu, Yifan Deng, Xinran Xu, Jingbo Xia, Zhongfei Zhang, Yuxin Cui, Xionghui Zhou, Feng Huang and Haitao Fu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Scientific Reports.

In The Last Decade

Shichao Liu

38 papers receiving 947 citations

Hit Papers

A multimodal deep learning framework for predicting drug–... 2020 2026 2022 2024 2020 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A multimodal deep learning framework for predicting drug–drug interaction events
    2020 281 citations Yang Qiu, Wen Zhang et al. Bioinformatics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shichao Liu China 16 645 625 201 167 105 45 966
Yanli Wang China 16 679 1.1× 661 1.1× 110 0.5× 97 0.6× 115 1.1× 41 1.1k
Thomas Seidel Austria 15 563 0.9× 513 0.8× 260 1.3× 68 0.4× 49 0.5× 53 1.0k
Yang Qiu China 13 503 0.8× 509 0.8× 159 0.8× 113 0.7× 98 0.9× 34 804
Masaaki Kotera Japan 22 909 1.4× 1.3k 2.0× 132 0.7× 83 0.5× 191 1.8× 52 1.7k
Guixia Liu China 17 765 1.2× 773 1.2× 121 0.6× 43 0.3× 123 1.2× 22 1.2k
Xiang Yue China 15 514 0.8× 772 1.2× 66 0.3× 332 2.0× 56 0.5× 40 1.2k
Xiaorui Su China 18 549 0.9× 769 1.2× 113 0.6× 156 0.9× 37 0.4× 43 1.0k
Dejun Jiang China 20 1.1k 1.7× 914 1.5× 634 3.2× 140 0.8× 45 0.4× 54 1.7k
Hongjian Li Hong Kong 18 894 1.4× 951 1.5× 348 1.7× 41 0.2× 41 0.4× 31 1.4k
Mélaine A. Kuenemann France 14 518 0.8× 544 0.9× 270 1.3× 78 0.5× 34 0.3× 23 994

Countries citing papers authored by Shichao Liu

Since Specialization
Citations

This map shows the geographic impact of Shichao Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shichao Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shichao Liu more than expected).

Fields of papers citing papers by Shichao Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shichao Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shichao Liu. The network helps show where Shichao Liu may publish in the future.

Co-authorship network of co-authors of Shichao Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Shichao Liu. A scholar is included among the top collaborators of Shichao Liu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shichao Liu. Shichao Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Liu, Shichao, et al.. (2025). Network pharmacology and molecular docking reveal the potential inner mechanism between sensitive skin and hyperpigmentation. SHILAP Revista de lepidopterología. 2(1). 100070–100070.
2.
Li, Yanyan, Siru Gao, Shengkai Zhao, et al.. (2025). Thermal responses under different exercise intensity and air temperature. Building and Environment. 271. 112591–112591. 4 indexed citations
3.
Yang, Jinlong, et al.. (2025). Physics-shielded deep reinforcement learning for safe energy management of microgrids with battery health consideration. Journal of the Franklin Institute. 362(16). 108112–108112.
4.
Sun, Yanling, Jiayi Zhang, Jing Zeng, et al.. (2025). An active protein from Dendrobium officinale residue: Protects the gastric mucosa and stabilized in the gastrointestinal tract. International Journal of Biological Macromolecules. 294. 139387–139387. 1 indexed citations
5.
Liu, Chao, et al.. (2025). Reliability analysis of subsea pipeline system based on fuzzy polymorphic bayesian network. Scientific Reports. 15(1). 11523–11523.
6.
7.
Guo, Xinyun, et al.. (2025). Cross-modal interaction between temperature and light color temperature on reading comprehension. Building and Environment. 274. 112751–112751. 1 indexed citations
8.
Wang, Chao, et al.. (2025). The influence of temperature and interior ambient lighting color on nighttime driving performance. Building and Environment. 275. 112815–112815. 3 indexed citations
9.
10.
11.
Lin, Shangchao, et al.. (2024). Experiment-Validated multiphysics modeling, generalizable deep learning and interpretable global sensitivity analyses for thermoelectric generators. International Journal of Heat and Mass Transfer. 229. 125711–125711. 3 indexed citations
12.
Li, Xinyue, et al.. (2024). Deep learning for drug‐drug interaction prediction: A comprehensive review. Quantitative Biology. 12(1). 30–52. 15 indexed citations
13.
Zhang, Jianhua, et al.. (2024). The synergistic effects of Guaiacum officinale and Rhodomyrtus tomentosa extracts in the treatment of acne vulgaris on sensitive skin. Journal of Cosmetic Dermatology. 23(10). 3356–3365. 3 indexed citations
14.
Liu, Shichao, et al.. (2023). CCSMP: an efficient closed contiguous sequential pattern mining algorithm with a pattern relation graph. Applied Intelligence. 53(24). 29723–29740.
15.
Fu, Haitao, Yuyang Wu, Feng Huang, et al.. (2023). HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction. Briefings in Bioinformatics. 24(5). 27 indexed citations
16.
Liu, Shichao, et al.. (2023). A pre-trained multi-representation fusion network for molecular property prediction. Information Fusion. 103. 102092–102092. 17 indexed citations
17.
Liu, Shichao, et al.. (2023). Multi-Relational Contrastive Learning Graph Neural Network for Drug-Drug Interaction Event Prediction. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 5339–5347. 37 indexed citations
18.
Liu, Shichao, et al.. (2022). Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction. Bioinformatics. 38(20). 4782–4789. 43 indexed citations
19.
Qiu, Yang, et al.. (2020). Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction. BMC Genomics. 21(S13). 867–867. 21 indexed citations
20.
Zhao, Xiaohan, Guangyan Zhang, Weiyang Li, et al.. (2019). PredLnc-GFStack: A Global Sequence Feature Based on a Stacked Ensemble Learning Method for Predicting lncRNAs from Transcripts. Genes. 10(9). 672–672. 20 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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