Ben Liao
Impact in
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- Computational Drug Discovery Methods
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- Machine Learning in Materials Science
Papers in
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- Computational Drug Discovery Methods 6
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- Protein Structure and Dynamics 4
- Bioinformatics and Genomic Networks 1
- Co-authors
- Tingjun Hou (6 shared papers)Chang‐Yu Hsieh (6 shared papers)Dejun Jiang (5 shared papers)Dongsheng Cao (5 shared papers)Guangyong Chen (3 shared papers)Chao Shen (3 shared papers)Zhenhua Wu (3 shared papers)Zhe Wang (1 shared paper)
- Journals
- Journal of Medicinal Chemistry (3 papers)Briefings in Bioinformatics (1 paper)Journal of Cheminformatics (1 paper)Journal of Materials Processing Technology (1 paper)Institutional Research Information System (Università degli Studi di Brescia) (1 paper)
In The Last Decade
Ben Liao
10 papers receiving 705 citations
Ben Liao's Hit Papers
Peers
Comparison fields: 5 of 95
- Computational Theory and Mathematics 493
- Materials Chemistry 301
- Molecular Biology 346
- Artificial Intelligence 121
- Biophysics 18
Countries citing papers authored by Ben Liao
This map shows the geographic impact of Ben Liao'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 Ben Liao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ben Liao more than expected).
Fields of papers citing papers by Ben Liao
This network shows the impact of papers produced by Ben Liao. 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 Ben Liao. The network helps show where Ben Liao may publish in the future.
Co-authors
The 25 scholars most cited alongside Ben Liao, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models Hit paper breakdown → | 2021 | 412 |
| 2 | 2021 | 158 | |
| 3 | 2021 | 53 | |
| 4 | 2019 | 40 | |
| 5 | 2022 | 22 | |
| 6 | 2022 | 19 | |
| 7 | Locality and Modularity in Abstract Argumentation | 2018 | 5 |
| 8 | 2023 | 3 | |
| 9 | 2022 | 3 | |
| 10 | 2009 | 2 |
About Ben Liao
Ben Liao is a scholar working on Computational Theory and Mathematics, Molecular Biology, Materials Chemistry, Artificial Intelligence and Organic Chemistry, having authored 10 papers that have together received 717 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (6 papers), Machine Learning in Materials Science (5 papers), Protein Structure and Dynamics (4 papers), Click Chemistry and Applications (1 paper), Water Systems and Optimization (1 paper), Bioinformatics and Genomic Networks (1 paper), Topic Modeling (1 paper) and Multi-Agent Systems and Negotiation (1 paper). The work is most often cited by research in Computational Theory and Mathematics (493 citations), Materials Chemistry (301 citations), Molecular Biology (346 citations), Artificial Intelligence (121 citations) and Biophysics (18 citations). Ben Liao has collaborated with scholars based in China, Taiwan and Macao. Frequent co-authors include Tingjun Hou, Chang‐Yu Hsieh, Dejun Jiang, Dongsheng Cao, Guangyong Chen, Chao Shen, Zhenhua Wu, Zhe Wang, Jian Wu and Jike Wang. Their work appears in journals such as Journal of Medicinal Chemistry, Briefings in Bioinformatics, Journal of Cheminformatics, Journal of Materials Processing Technology and Institutional Research Information System (Università degli Studi di Brescia).
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.