Shidi Tang

437 total citations · 1 hit paper
12 papers, 227 citations indexed

About

Shidi Tang is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Shidi Tang has authored 12 papers receiving a total of 227 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 4 papers in Computational Theory and Mathematics and 3 papers in Artificial Intelligence. Recurrent topics in Shidi Tang's work include Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (3 papers) and Machine Learning in Materials Science (2 papers). Shidi Tang is often cited by papers focused on Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (3 papers) and Machine Learning in Materials Science (2 papers). Shidi Tang collaborates with scholars based in China, Belgium and United Kingdom. Shidi Tang's co-authors include Jiansheng Wu, Ming Ling, Haifeng Hu, Ruiqi Chen, Qinqin Huang, Qinglin Zhang, He Zhang, Ziheng Lu, Tie‐Yan Liu and Fusong Ju and has published in prestigious journals such as Molecules, Journal of Chemical Information and Modeling and IEEE Communications Letters.

In The Last Decade

Shidi Tang

9 papers receiving 225 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.

Predicting equilibrium distributions for molecular systems with deep learning

2024 59 citations Shuxin Zheng, Yu Shi et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shidi Tang China	6	122	89	40	19	18	12	227
Ashwin Dhakal United States	6	123 1.0×	96 1.1×	35 0.9×	14 0.7×	4 0.2×	10	251
Ada Sedova United States	8	105 0.9×	67 0.8×	44 1.1×	15 0.8×	21 1.2×	26	196
Ngan K. Tran United States	5	179 1.5×	140 1.6×	53 1.3×	6 0.3×	4 0.2×	8	291
Fei Tong China	7	204 1.7×	140 1.6×	53 1.3×	6 0.3×	4 0.2×	16	359
Paul S. Bond United Kingdom	8	106 0.9×	25 0.3×	45 1.1×	22 1.2×	15 0.8×	13	201
José Ignacio Abreu Cuba	9	161 1.3×	108 1.2×	59 1.5×	14 0.7×	9 0.5×	35	391
Xiaochu Tong China	7	164 1.3×	185 2.1×	82 2.0×	4 0.2×	4 0.2×	14	293
Juan Mei China	8	210 1.7×	36 0.4×	13 0.3×	6 0.3×	6 0.3×	26	350
Enric Gibert Spain	10	104 0.9×	126 1.4×	25 0.6×	54 2.8×	140 7.8×	29	369
Rui Duan United States	9	89 0.7×	30 0.3×	19 0.5×	19 1.0×	17 0.9×	22	224

Countries citing papers authored by Shidi Tang

Since Specialization

Citations

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

Fields of papers citing papers by Shidi Tang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shidi Tang

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

12 of 12 papers shown

1.

Ling, Ming, Chuanzhao Zhang, Shidi Tang, Ruiqi Chen, & Yuanming Zhu. (2025). EEVS: Redeploying Discarded Smartphones for Economic and Ecological Drug Molecules Virtual Screening. IEEE Transactions on Sustainable Computing. 1–13. 1 indexed citations

2.

Chen, Ruiqi, Yue-Long Lyu, Han Bao, et al.. (2025). FPGA-Based Approximate Multiplier for FP8. VUBIR (Vrije Universiteit Brussel). 1–9.

3.

Chen, Ruiqi, et al.. (2025). ATE-GCN: An FPGA-Based Graph Convolutional Network Accelerator with Asymmetrical Ternary Quantization. VUBIR (Vrije Universiteit Brussel). 1–6.

4.

Zheng, Shuxin, Yu Shi, Ziheng Lu, et al.. (2024). Predicting equilibrium distributions for molecular systems with deep learning. Nature Machine Intelligence. 6(5). 558–567. 59 indexed citations breakdown →

5.

Ling, Ming, et al.. (2024). Vina-FPGA-Cluster: Multi-FPGA Based Molecular Docking Tool With High-Accuracy and Multi-Level Parallelism. IEEE Transactions on Biomedical Circuits and Systems. 18(6). 1321–1337. 2 indexed citations

6.

Tang, Shidi, et al.. (2024). Vina-GPU 2.1: Towards Further Optimizing Docking Speed and Precision of AutoDock Vina and Its Derivatives. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(6). 2382–2393. 6 indexed citations

7.

Tang, Shidi, X. R. Zhou, & Ming Ling. (2024). Modeling Equivariant Neural Networks for Hardware Acceleration, a Case Study on the Molecular Docking Tool DiffDock. 1–6.

8.

Tang, Shidi, et al.. (2023). Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units. Journal of Chemical Information and Modeling. 63(7). 1982–1998. 57 indexed citations

9.

Zhou, X. R., et al.. (2023). Effectiveness Analysis of Multiple Initial States Simulated Annealing Algorithm, a Case Study on the Molecular Docking Tool AutoDock Vina. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(6). 3830–3841. 9 indexed citations

10.

Zhou, Xingxing, et al.. (2022). Effectiveness Analysis of Multiple Initial States Simulated Annealing Algorithm, a Case Study on the Molecular Docking Tool Autodock Vina. SSRN Electronic Journal. 3 indexed citations

11.

Tang, Shidi, et al.. (2022). Accelerating AutoDock Vina with GPUs. Molecules. 27(9). 3041–3041. 74 indexed citations

12.

Tang, Shidi, et al.. (2019). A Fast Approximate Check Polytope Projection Algorithm for ADMM Decoding of LDPC Codes. IEEE Communications Letters. 23(9). 1520–1523. 16 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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