Max K. Leong

1.5k total citations · 1 hit paper
40 papers, 1.2k citations indexed

About

Max K. Leong is a scholar working on Computational Theory and Mathematics, Oncology and Molecular Biology. According to data from OpenAlex, Max K. Leong has authored 40 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computational Theory and Mathematics, 8 papers in Oncology and 7 papers in Molecular Biology. Recurrent topics in Max K. Leong's work include Computational Drug Discovery Methods (16 papers), Analytical Chemistry and Chromatography (5 papers) and Drug Transport and Resistance Mechanisms (5 papers). Max K. Leong is often cited by papers focused on Computational Drug Discovery Methods (16 papers), Analytical Chemistry and Chromatography (5 papers) and Drug Transport and Resistance Mechanisms (5 papers). Max K. Leong collaborates with scholars based in Taiwan, Vietnam and China. Max K. Leong's co-authors include Ching‐Feng Weng, Shian‐Ren Lin, Ping‐Jyun Sung, Henrich Cheng, Che‐Fang Hsu, Jian‐Chyi Chen, V. S. Mastryukov, James E. Boggs, Dinh‐Chuong Pham and Hongbin Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and The Journal of Physical Chemistry.

In The Last Decade

Max K. Leong

39 papers receiving 1.2k citations

Hit Papers

Natural compounds as potential adjuvants to cancer therap... 2019 2026 2021 2023 2019 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Max K. Leong Taiwan 21 436 286 195 148 131 40 1.2k
Khac‐Minh Thai Vietnam 19 398 0.9× 319 1.1× 371 1.9× 116 0.8× 64 0.5× 60 1.0k
Maria Maddalena Cavalluzzi Italy 19 515 1.2× 121 0.4× 300 1.5× 66 0.4× 96 0.7× 65 1.1k
Malgorzata N. Drwal Germany 13 510 1.2× 542 1.9× 253 1.3× 83 0.6× 118 0.9× 18 1.1k
Zhi‐Jiang Yao China 10 672 1.5× 673 2.4× 228 1.2× 73 0.5× 180 1.4× 10 1.4k
Anwar Rayan Israel 21 762 1.7× 345 1.2× 147 0.8× 70 0.5× 131 1.0× 53 1.5k
Sarah Naomi Bolz Germany 8 757 1.7× 300 1.0× 249 1.3× 137 0.9× 98 0.7× 12 1.4k
Giuseppe Fracchiolla Italy 26 958 2.2× 93 0.3× 303 1.6× 106 0.7× 102 0.8× 74 1.7k
Francis C. K. Chiu Australia 21 524 1.2× 332 1.2× 459 2.4× 128 0.9× 129 1.0× 45 1.8k
Jiazhong Li China 25 655 1.5× 368 1.3× 281 1.4× 195 1.3× 74 0.6× 84 1.4k
Yuanxin Tian China 27 1.1k 2.6× 144 0.5× 230 1.2× 125 0.8× 187 1.4× 91 2.0k

Countries citing papers authored by Max K. Leong

Since Specialization
Citations

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

Fields of papers citing papers by Max K. Leong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Max K. Leong. 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 Max K. Leong. The network helps show where Max K. Leong may publish in the future.

Co-authorship network of co-authors of Max K. Leong

This figure shows the co-authorship network connecting the top 25 collaborators of Max K. Leong. A scholar is included among the top collaborators of Max K. Leong 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 Max K. Leong. Max K. Leong 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.
Leong, Max K., et al.. (2025). Modeling skin sensitization: hierarchical support vector regression−based prediction of lysine depletion in DPRA. Chemico-Biological Interactions. 420. 111714–111714. 1 indexed citations
2.
Leong, Max K., et al.. (2025). A novel in silico approach for predicting unbound brain-to-plasma ratio using machine learning-based support vector regression. Computers in Biology and Medicine. 192(Pt B). 110366–110366.
3.
Weng, Ching‐Feng, et al.. (2024). Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA. Toxicology. 503. 153739–153739. 3 indexed citations
4.
Leong, Max K., et al.. (2022). Using Machine Learning-Based Hierarchical Support Vector Regression Approach to Predict Skin Permeability. SHILAP Revista de lepidopterología. 132–132. 1 indexed citations
5.
Weng, Ching‐Feng, et al.. (2022). In Silico Prediction of Skin Permeability Using a Two-QSAR Approach. Pharmaceutics. 14(5). 961–961. 11 indexed citations
6.
Lin, Shian‐Ren, et al.. (2021). Mangosteen xanthone γ-mangostin exerts lowering blood glucose effect with potentiating insulin sensitivity through the mediation of AMPK/PPARγ. Biomedicine & Pharmacotherapy. 144. 112333–112333. 22 indexed citations
7.
Weng, Ching‐Feng, et al.. (2021). In silico Prediction of Skin Sensitization: Quo vadis?. Frontiers in Pharmacology. 12. 655771–655771. 28 indexed citations
8.
Pham, Dinh‐Chuong, et al.. (2021). In Silico Approaches to Identify Polyphenol Compounds as α-Glucosidase and α-Amylase Inhibitors against Type-II Diabetes. Biomolecules. 11(12). 1877–1877. 96 indexed citations
9.
Leong, Max K., et al.. (2017). Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme. Scientific Reports. 7(1). 40053–40053. 21 indexed citations
10.
11.
Leong, Max K., et al.. (2016). In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach. Toxicology in Vitro. 40. 102–114. 28 indexed citations
12.
Tsai, Fu‐Yuan, et al.. (2014). In Silico Prediction of Inhibition of Promiscuous Breast Cancer Resistance Protein (BCRP/ABCG2). PLoS ONE. 9(3). e90689–e90689. 14 indexed citations
13.
Leong, Max K., et al.. (2012). Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme. PLoS ONE. 7(3). e33829–e33829. 22 indexed citations
14.
Leong, Max K., et al.. (2010). Predicting Mutagenicity of Aromatic Amines by Various Machine Learning Approaches. Toxicological Sciences. 116(2). 498–513. 33 indexed citations
15.
Leong, Max K., et al.. (2008). Prediction of Cytochrome P450 2B6-Substrate Interactions Using Pharmacophore Ensemble/Support Vector Machine (PhE/SVM) Approach. Medicinal Chemistry. 4(4). 396–406. 17 indexed citations
16.
Leong, Max K., et al.. (2008). Prediction of human cytochrome P450 2B6‐substrate interactions using hierarchical support vector regression approach. Journal of Computational Chemistry. 30(12). 1899–1909. 21 indexed citations
17.
Leong, Max K., et al.. (2008). Au(i)-benzimidazole/imidazole complexes. Liquid crystals and nanomaterials. Dalton Transactions. 1924–1924. 40 indexed citations
18.
Leong, Max K., et al.. (2007). Selection and characterization of lipase abzyme from phage displayed antibody libraries. Biochemical and Biophysical Research Communications. 361(3). 567–573. 7 indexed citations
19.
Lyu, Ping‐Chiang, et al.. (2003). 3D-QSAR studies on PU3 analogues by comparative molecular field analysis. Bioorganic & Medicinal Chemistry Letters. 14(3). 731–734. 4 indexed citations
20.
HAKIMELAHI, G. H., Pai‐Chi Li, Ali Akbar Moosavi‐Movahedi, et al.. (2003). Application of the Barton photochemical reaction in the synthesis of 1-dethia-3-aza-1-carba-2-oxacephem: a novel agent against resistant pathogenic microorganisms. Organic & Biomolecular Chemistry. 1(14). 2461–2461. 12 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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