Bo‐Han Su

577 total citations
17 papers, 432 citations indexed

About

Bo‐Han Su is a scholar working on Computational Theory and Mathematics, Molecular Biology and Pharmacology. According to data from OpenAlex, Bo‐Han Su has authored 17 papers receiving a total of 432 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computational Theory and Mathematics, 12 papers in Molecular Biology and 3 papers in Pharmacology. Recurrent topics in Bo‐Han Su's work include Computational Drug Discovery Methods (15 papers), Metabolomics and Mass Spectrometry Studies (5 papers) and Receptor Mechanisms and Signaling (3 papers). Bo‐Han Su is often cited by papers focused on Computational Drug Discovery Methods (15 papers), Metabolomics and Mass Spectrometry Studies (5 papers) and Receptor Mechanisms and Signaling (3 papers). Bo‐Han Su collaborates with scholars based in Taiwan and United States. Bo‐Han Su's co-authors include Yufeng Jane Tseng, Emilio Xavier Esposito, A. J. Hopfinger, Chien Lee, Olivia A. Lin, Chieh Lin, Kuo-Hsiang Hsu, Alex Renn, Cheng‐Fu Chou and Peihua Wang and has published in prestigious journals such as Bioinformatics, PLoS ONE and Molecules.

In The Last Decade

Bo‐Han Su

16 papers receiving 425 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bo‐Han Su Taiwan	11	279	233	84	69	48	17	432
Sampada A. Shahane United States	11	304 1.1×	312 1.3×	83 1.0×	50 0.7×	57 1.2×	13	595
Tongan Zhao United States	9	375 1.3×	337 1.4×	95 1.1×	62 0.9×	12 0.3×	12	730
Emilio Xavier Esposito United States	13	438 1.6×	376 1.6×	137 1.6×	30 0.4×	49 1.0×	20	661
Britta Nisius Germany	11	467 1.7×	428 1.8×	84 1.0×	50 0.7×	20 0.4×	18	613
Jianlong Peng China	12	281 1.0×	390 1.7×	38 0.5×	84 1.2×	27 0.6×	14	688
K. V. Karapetyan United States	4	448 1.6×	448 1.9×	72 0.9×	80 1.2×	12 0.3×	7	715
Alban Lepailleur France	14	194 0.7×	255 1.1×	31 0.4×	36 0.5×	20 0.4×	32	619
Thierry Hanser United Kingdom	10	225 0.8×	124 0.5×	68 0.8×	26 0.4×	7 0.1×	16	341
Avid M. Afzal United Kingdom	12	247 0.9×	211 0.9×	59 0.7×	56 0.8×	4 0.1×	20	352
Christof H. Schwab Germany	9	279 1.0×	184 0.8×	64 0.8×	46 0.7×	4 0.1×	17	431

Countries citing papers authored by Bo‐Han Su

Since Specialization

Citations

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

Fields of papers citing papers by Bo‐Han Su

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bo‐Han Su. 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 Bo‐Han Su. The network helps show where Bo‐Han Su may publish in the future.

Co-authorship network of co-authors of Bo‐Han Su

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

All Works

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17 of 17 papers shown

Wang, Peihua, et al.. (2025). A robust and interpretable graph neural network-based protocol for predicting p-glycoprotein substrates. Briefings in Bioinformatics. 26(4).

Lee, Chien, Bo‐Han Su, & Yufeng Jane Tseng. (2022). Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors. Briefings in Bioinformatics. 23(5). 72 indexed citations

Su, Bo‐Han, et al.. (2021). Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power. Briefings in Bioinformatics. 23(1). 39 indexed citations

Su, Bo‐Han, et al.. (2020). Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development. Briefings in Bioinformatics. 22(3). 27 indexed citations

Renn, Alex, et al.. (2020). Advances in the prediction of mouse liver microsomal studies: From machine learning to deep learning. Wiley Interdisciplinary Reviews Computational Molecular Science. 11(1). 7 indexed citations

Su, Bo‐Han, et al.. (2017). G.A.M.E.: GPU-accelerated mixture elucidator. Journal of Cheminformatics. 9(1). 50–50. 15 indexed citations

Su, Bo‐Han, et al.. (2017). NP-StructurePredictor: Prediction of Unknown Natural Products in Plant Mixtures. Journal of Chemical Information and Modeling. 57(12). 3138–3148. 5 indexed citations

Hsu, Kuo-Hsiang, et al.. (2016). Mutagenicity in a Molecule: Identification of Core Structural Features of Mutagenicity Using a Scaffold Analysis. PLoS ONE. 11(2). e0148900–e0148900. 29 indexed citations

Esposito, Emilio Xavier, et al.. (2015). Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models. Toxicology and Applied Pharmacology. 288(1). 52–62. 8 indexed citations

10.

Su, Bo‐Han, et al.. (2015). CypRules: a rule-based P450 inhibition prediction server. Bioinformatics. 31(11). 1869–1871. 36 indexed citations

11.

Su, Bo‐Han, et al.. (2015). Rule-Based Classification Models of Molecular Autofluorescence. Journal of Chemical Information and Modeling. 55(2). 434–445. 10 indexed citations

12.

Su, Bo‐Han, et al.. (2015). Rule-Based Prediction Models of Cytochrome P450 Inhibition. Journal of Chemical Information and Modeling. 55(7). 1426–1434. 28 indexed citations

13.

Su, Bo‐Han, et al.. (2013). Template-Based de Novo Design for Type II Kinase Inhibitors and Its Extended Application to Acetylcholinesterase Inhibitors. Molecules. 18(11). 13487–13509. 3 indexed citations

14.

Su, Bo‐Han, et al.. (2012). Predictive Toxicology Modeling: Protocols for Exploring hERG Classification and Tetrahymena pyriformis End Point Predictions. Journal of Chemical Information and Modeling. 52(6). 1660–1673. 22 indexed citations

15.

Su, Bo‐Han, et al.. (2012). Dependence of QSAR Models on the Selection of Trial Descriptor Sets: A Demonstration Using Nanotoxicity Endpoints of Decorated Nanotubes. Journal of Chemical Information and Modeling. 53(1). 142–158. 35 indexed citations

16.

Su, Bo‐Han, et al.. (2011). A Comprehensive Support Vector Machine Binary hERG Classification Model Based on Extensive but Biased End Point hERG Data Sets. Chemical Research in Toxicology. 24(6). 934–949. 37 indexed citations

17.

Su, Bo‐Han, et al.. (2010). In Silico Binary Classification QSAR Models Based on 4D-Fingerprints and MOE Descriptors for Prediction of hERG Blockage. Journal of Chemical Information and Modeling. 50(7). 1304–1318. 59 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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