Dedi Wang

419 total citations
9 papers, 224 citations indexed

About

Dedi Wang is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Dedi Wang has authored 9 papers receiving a total of 224 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 4 papers in Materials Chemistry and 3 papers in Computational Theory and Mathematics. Recurrent topics in Dedi Wang's work include Protein Structure and Dynamics (8 papers), Machine Learning in Materials Science (4 papers) and Computational Drug Discovery Methods (3 papers). Dedi Wang is often cited by papers focused on Protein Structure and Dynamics (8 papers), Machine Learning in Materials Science (4 papers) and Computational Drug Discovery Methods (3 papers). Dedi Wang collaborates with scholars based in United States. Dedi Wang's co-authors include Pratyush Tiwary, Shashank Pant, John D. Weeks, Yihang Wang and Markus A. Seeliger and has published in prestigious journals such as The Journal of Chemical Physics, The Journal of Physical Chemistry B and Journal of Chemical Theory and Computation.

In The Last Decade

Dedi Wang

9 papers receiving 224 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dedi Wang United States	7	171	104	63	32	24	9	224
Zachary Smith United States	8	158 0.9×	95 0.9×	59 0.9×	34 1.1×	25 1.0×	12	204
Jakub Rydzewski Poland	9	189 1.1×	87 0.8×	52 0.8×	32 1.0×	17 0.7×	25	248
Olivier Adjoua France	11	130 0.8×	100 1.0×	72 1.1×	74 2.3×	19 0.8×	16	259
Dominik Lemm Germany	5	141 0.8×	168 1.6×	60 1.0×	25 0.8×	9 0.4×	7	239
George Boxer United States	2	265 1.5×	106 1.0×	35 0.6×	62 1.9×	70 2.9×	4	316
Brian Olson United States	11	246 1.4×	126 1.2×	43 0.7×	7 0.2×	39 1.6×	13	303
Sergey N. Pozdnyakov Switzerland	8	61 0.4×	277 2.7×	157 2.5×	27 0.8×	17 0.7×	13	296
Hannah M. Baumann United States	5	96 0.6×	35 0.3×	70 1.1×	21 0.7×	13 0.5×	8	138
Levi N. Naden United States	5	70 0.4×	67 0.6×	39 0.6×	74 2.3×	14 0.6×	9	156
Nicholas J. Browning Switzerland	6	49 0.3×	123 1.2×	64 1.0×	53 1.7×	19 0.8×	8	200

Countries citing papers authored by Dedi Wang

Since Specialization

Citations

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

Fields of papers citing papers by Dedi Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dedi Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Dedi Wang. A scholar is included among the top collaborators of Dedi Wang 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 Dedi Wang. Dedi Wang 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:

9 of 9 papers shown

1.

Wang, Dedi, et al.. (2025). Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence–based methods. Current Opinion in Structural Biology. 91. 103000–103000. 16 indexed citations

2.

Wang, Dedi, et al.. (2024). Calculating Protein–Ligand Residence Times through State Predictive Information Bottleneck Based Enhanced Sampling. Journal of Chemical Theory and Computation. 20(14). 6341–6349. 8 indexed citations

3.

Wang, Dedi & Pratyush Tiwary. (2024). Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck. Journal of Chemical Theory and Computation. 20(23). 10371–10383. 5 indexed citations

4.

Wang, Dedi, et al.. (2024). A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics. Digital Discovery. 4(1). 211–221. 6 indexed citations

5.

Wang, Dedi, et al.. (2024). From Latent Dynamics to Meaningful Representations. Journal of Chemical Theory and Computation. 20(9). 3503–3513. 8 indexed citations

6.

Wang, Dedi, et al.. (2023). AlphaFold2-RAVE: From Sequence to Boltzmann Ranking. Journal of Chemical Theory and Computation. 19(14). 4351–4354. 58 indexed citations

7.

Wang, Dedi, et al.. (2022). Influence of Long-Range Forces on the Transition States and Dynamics of NaCl Ion-Pair Dissociation in Water. The Journal of Physical Chemistry B. 126(2). 545–551. 12 indexed citations

8.

Wang, Dedi, et al.. (2022). Accelerating All-Atom Simulations and Gaining Mechanistic Understanding of Biophysical Systems through State Predictive Information Bottleneck. Journal of Chemical Theory and Computation. 18(5). 3231–3238. 39 indexed citations

9.

Wang, Dedi & Pratyush Tiwary. (2021). State predictive information bottleneck. The Journal of Chemical Physics. 154(13). 134111–134111. 72 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.

Explore authors with similar magnitude of impact