Wen Torng

1.6k total citations · 1 hit paper
7 papers, 1.0k citations indexed

About

Wen Torng is a scholar working on Molecular Biology, Computational Theory and Mathematics and Biomedical Engineering. According to data from OpenAlex, Wen Torng has authored 7 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 4 papers in Computational Theory and Mathematics and 2 papers in Biomedical Engineering. Recurrent topics in Wen Torng's work include Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). Wen Torng is often cited by papers focused on Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). Wen Torng collaborates with scholars based in United States, Switzerland and Germany. Wen Torng's co-authors include Russ B. Altman, Stefano Rensi, Kung‐Bin Sung, P. C. Kuo, Chau‐Hwang Lee, Dario Neri, Samuele Cazzamalli, Jessica Xu, Jianwen A. Feng and Christian Bock and has published in prestigious journals such as Bioinformatics, PLoS ONE and BMC Bioinformatics.

In The Last Decade

Wen Torng

7 papers receiving 1.0k citations

Hit Papers

align trajectories

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.

Machine learning in chemoinformatics and drug discovery

2018 629 citations Stefano Rensi, Wen Torng et al. Drug Discovery Today profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wen Torng United States	7	715	632	337	74	70	7	1.0k
Jike Wang China	18	753 1.1×	660 1.0×	458 1.4×	77 1.0×	57 0.8×	52	1.1k
Yafeng Deng China	21	528 0.7×	624 1.0×	258 0.8×	71 1.0×	57 0.8×	56	1.2k
Eloy Félix United Kingdom	6	625 0.9×	545 0.9×	230 0.7×	88 1.2×	68 1.0×	11	1.0k
Duc Duy Nguyen United States	15	1.0k 1.4×	761 1.2×	384 1.1×	88 1.2×	61 0.9×	26	1.3k
Kaifu Gao United States	17	682 1.0×	659 1.0×	257 0.8×	60 0.8×	105 1.5×	30	1.3k
Miha Škalič Spain	9	844 1.2×	1.1k 1.7×	445 1.3×	98 1.3×	53 0.8×	11	1.4k
Alexios Koutsoukas United Kingdom	13	564 0.8×	487 0.8×	225 0.7×	115 1.6×	66 0.9×	26	906
Lukas Friedrich Switzerland	13	574 0.8×	490 0.8×	324 1.0×	124 1.7×	89 1.3×	25	940
Tunca Doğan Türkiye	13	615 0.9×	859 1.4×	220 0.7×	65 0.9×	34 0.5×	28	1.2k
Nils Weskamp Germany	14	587 0.8×	603 1.0×	216 0.6×	96 1.3×	60 0.9×	21	905

Countries citing papers authored by Wen Torng

Since Specialization

Citations

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

Fields of papers citing papers by Wen Torng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wen Torng

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

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Torng, Wen, Sebastian Oehler, Jin Xu, et al.. (2023). Deep Learning Approach for the Discovery of Tumor-Targeting Small Organic Ligands from DNA-Encoded Chemical Libraries. ACS Omega. 8(28). 25090–25100. 9 indexed citations

2.

Torng, Wen & Russ B. Altman. (2019). Graph Convolutional Neural Networks for Predicting Drug-Target Interactions. Journal of Chemical Information and Modeling. 59(10). 4131–4149. 230 indexed citations

3.

Rensi, Stefano, et al.. (2018). Machine learning in chemoinformatics and drug discovery. Drug Discovery Today. 23(8). 1538–1546. 629 indexed citations breakdown →

4.

Torng, Wen & Russ B. Altman. (2018). High precision protein functional site detection using 3D convolutional neural networks. Bioinformatics. 35(9). 1503–1512. 51 indexed citations

5.

Liu, Tianyun, Wen Torng, Aleix Lafita, et al.. (2017). Biological and functional relevance of CASP predictions. Proteins Structure Function and Bioinformatics. 86(S1). 374–386. 8 indexed citations

6.

Torng, Wen & Russ B. Altman. (2017). 3D deep convolutional neural networks for amino acid environment similarity analysis. BMC Bioinformatics. 18(1). 302–302. 100 indexed citations

7.

Torng, Wen, et al.. (2014). Substrate Stiffness Regulates Filopodial Activities in Lung Cancer Cells. PLoS ONE. 9(2). e89767–e89767. 22 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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