Jiashun Mao

433 total citations
16 papers, 261 citations indexed

About

Jiashun Mao is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Jiashun Mao has authored 16 papers receiving a total of 261 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 9 papers in Computational Theory and Mathematics and 5 papers in Materials Chemistry. Recurrent topics in Jiashun Mao's work include Computational Drug Discovery Methods (9 papers), Machine Learning in Materials Science (5 papers) and Chemical Synthesis and Analysis (2 papers). Jiashun Mao is often cited by papers focused on Computational Drug Discovery Methods (9 papers), Machine Learning in Materials Science (5 papers) and Chemical Synthesis and Analysis (2 papers). Jiashun Mao collaborates with scholars based in China, South Korea and Pakistan. Jiashun Mao's co-authors include Kyoung Tai No, Jianmin Wang, Hyeon-Nae Jeon, Liang Sun, Amir Zeb, Haiyan Jin, Kwang‐Hwi Cho, Guanyu Wang, Yunyun Wang and Guangming Chen and has published in prestigious journals such as International Journal of Molecular Sciences, Pattern Recognition and Journal of Chemical Theory and Computation.

In The Last Decade

Jiashun Mao

15 papers receiving 256 citations

Peers

Jiashun Mao
Francois Berenger Japan
Cyrill Brunner Switzerland
Stefania Pfeiffer‐Marek Germany
Claudia S. Neuhaus Switzerland
Yana Rose United States
Tong He China
Syed Ausaf Ali India
Jennifer Loschwitz Germany
Dmitry Suplatov Russia
A. Beatriz Garmendia-Doval United Kingdom
Francois Berenger Japan
Jiashun Mao
Citations per year, relative to Jiashun Mao Jiashun Mao (= 1×) peers Francois Berenger

Countries citing papers authored by Jiashun Mao

Since Specialization
Citations

This map shows the geographic impact of Jiashun Mao'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 Jiashun Mao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jiashun Mao more than expected).

Fields of papers citing papers by Jiashun Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jiashun Mao. 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 Jiashun Mao. The network helps show where Jiashun Mao may publish in the future.

Co-authorship network of co-authors of Jiashun Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Jiashun Mao. A scholar is included among the top collaborators of Jiashun Mao 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 Jiashun Mao. Jiashun Mao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Lu, Q. W., et al.. (2025). Comparison study of dominant molecular sequence representation based on diffusion model. Journal of Computer-Aided Molecular Design. 39(1). 54–54. 1 indexed citations
2.
Mao, Jiashun, et al.. (2025). MolRL: Self-supervised molecular image representation learning via graph structure bootstrapping. Pattern Recognition. 172. 112773–112773.
3.
4.
Wang, Jianmin, Jiashun Mao, Chunyan Li, et al.. (2024). Interface-aware molecular generative framework for protein–protein interaction modulators. Journal of Cheminformatics. 16(1). 142–142. 5 indexed citations
5.
Wang, Jianmin, Zixu Wang, Wei Long, et al.. (2024). Diffusion-based generative drug-like molecular editing with chemical natural language. Journal of Pharmaceutical Analysis. 15(6). 101137–101137. 4 indexed citations
6.
Wang, Jianmin, Xun Wang, Yanyi Chu, et al.. (2024). Exploring the Conformational Ensembles of Protein–Protein Complex with Transformer-Based Generative Model. Journal of Chemical Theory and Computation. 20(11). 4469–4480. 8 indexed citations
7.
Kim, Jongwan, Haiyan Jin, Sungho Moon, et al.. (2024). Leveraging the Fragment Molecular Orbital and MM-GBSA Methods in Virtual Screening for the Discovery of Novel Non-Covalent Inhibitors Targeting the TEAD Lipid Binding Pocket. International Journal of Molecular Sciences. 25(10). 5358–5358. 4 indexed citations
8.
Wang, Jianmin, et al.. (2023). Explore drug-like space with deep generative models. Methods. 210. 52–59. 21 indexed citations
9.
Mao, Jiashun, Jianmin Wang, Amir Zeb, et al.. (2023). Transformer-Based Molecular Generative Model for Antiviral Drug Design. Journal of Chemical Information and Modeling. 64(7). 2733–2745. 38 indexed citations
10.
11.
Wang, Jianmin, Yanyi Chu, Jiashun Mao, et al.. (2022). De novo molecular design with deep molecular generative models for PPI inhibitors. Briefings in Bioinformatics. 23(4). 42 indexed citations
12.
Mao, Jiashun, et al.. (2022). Application of a deep generative model produces novel and diverse functional peptides against microbial resistance. Computational and Structural Biotechnology Journal. 21. 463–471. 20 indexed citations
13.
Mao, Jiashun, Xiao Zhang, Liang Sun, et al.. (2021). Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models. iScience. 24(9). 103052–103052. 87 indexed citations
14.
15.
Sun, Liang, Xinyu Li, Jun Pan, et al.. (2019). Seeking mTORC1 Inhibitors Through Molecular Dynamics Simulation of Arginine Analogs Inhibiting CASTOR1. Cancer Genomics & Proteomics. 16(6). 465–479. 4 indexed citations
16.
Sun, Liang, Jiashun Mao, Ying Zhao, et al.. (2017). Coarse-grained molecular dynamics simulation of interactions between cyclic lipopeptide Bacillomycin D and cell membranes. Molecular Simulation. 44(5). 364–376. 11 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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