Jiacai Yi

3.8k total citations · 2 hit papers
15 papers, 2.4k citations indexed

About

Jiacai Yi is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology. According to data from OpenAlex, Jiacai Yi has authored 15 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computational Theory and Mathematics, 8 papers in Materials Chemistry and 7 papers in Molecular Biology. Recurrent topics in Jiacai Yi's work include Computational Drug Discovery Methods (10 papers), Machine Learning in Materials Science (8 papers) and Pharmacogenetics and Drug Metabolism (3 papers). Jiacai Yi is often cited by papers focused on Computational Drug Discovery Methods (10 papers), Machine Learning in Materials Science (8 papers) and Pharmacogenetics and Drug Metabolism (3 papers). Jiacai Yi collaborates with scholars based in China, Hong Kong and United States. Jiacai Yi's co-authors include Chengkun Wu, Dongsheng Cao, Tingjun Hou, Zhenhua Wu, Xiangxiang Zeng, Zhijiang Yang, Chang‐Yu Hsieh, Aiping Lü, Xiang Chen and Guo‐Li Xiong and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Nature Protocols.

In The Last Decade

Jiacai Yi

13 papers receiving 2.3k 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.

ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

2021 1.7k citations Guo‐Li Xiong, Zhenhua Wu et al. Nucleic Acids Research profile →
ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support

2024 349 citations Li Fu, Shaohua Shi et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jiacai Yi China	10	1.1k	945	552	246	245	15	2.4k
Zhijiang Yang China	11	975 0.9×	950 1.0×	496 0.9×	238 1.0×	272 1.1×	24	2.2k
Guo‐Li Xiong China	9	926 0.9×	796 0.8×	474 0.9×	219 0.9×	150 0.6×	15	2.0k
Li Fu China	13	827 0.8×	772 0.8×	501 0.9×	209 0.8×	153 0.6×	23	2.1k
Ningning Wang China	15	988 0.9×	1.0k 1.1×	369 0.7×	263 1.1×	227 0.9×	35	2.2k
Lixia Sun China	11	728 0.7×	935 1.0×	452 0.8×	242 1.0×	148 0.6×	16	1.9k
Luca Pinzi Italy	17	1.2k 1.1×	802 0.8×	411 0.7×	285 1.2×	90 0.4×	42	2.2k
Martin Smieško Switzerland	27	1.0k 1.0×	560 0.6×	445 0.8×	261 1.1×	134 0.5×	102	2.1k
Sebastian Salentin Germany	13	1.8k 1.7×	851 0.9×	608 1.1×	213 0.9×	209 0.9×	15	3.2k
Hongbin Yang China	28	1.3k 1.2×	1.5k 1.6×	512 0.9×	419 1.7×	256 1.0×	70	3.2k
Anna K. Schrey Germany	14	1.0k 1.0×	873 0.9×	718 1.3×	257 1.0×	126 0.5×	24	2.5k

Countries citing papers authored by Jiacai Yi

Since Specialization

Citations

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

Fields of papers citing papers by Jiacai Yi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiacai Yi

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

All Works

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15 of 15 papers shown

Yi, Jiacai, Dejun Jiang, Chengkun Wu, et al.. (2025). Pushing the boundaries of few-shot learning for low-data drug discovery with a Bayesian meta-learning hypernetwork framework. Briefings in Bioinformatics. 26(4).

Li, Ting, Jiacai Yi, Hui Li, et al.. (2025). Decoding the limits of deep learning in molecular docking for drug discovery. Chemical Science. 16(37). 17374–17390. 2 indexed citations

Shi, Shaohua, Li Fu, Jiacai Yi, et al.. (2024). ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery. Nucleic Acids Research. 52(W1). W439–W449.

Yi, Jiacai, Ziyi Yang, Wentao Zhao, et al.. (2024). ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization. Briefings in Bioinformatics. 25(2). 10 indexed citations

Yi, Jiacai, Shaohua Shi, Li Fu, et al.. (2024). OptADMET: a web-based tool for substructure modifications to improve ADMET properties of lead compounds. Nature Protocols. 19(4). 1105–1121. 23 indexed citations

Fu, Li, Shaohua Shi, Jiacai Yi, et al.. (2024). ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Research. 52(W1). W422–W431. 349 indexed citations breakdown →

Yi, Jiacai, et al.. (2024). DDInter 2.0: an enhanced drug interaction resource with expanded data coverage, new interaction types, and improved user interface. Nucleic Acids Research. 53(D1). D1356–D1362. 4 indexed citations

Yi, Jiacai, et al.. (2024). ISTransbase: an online database for inhibitor and substrate of drug transporters. Database. 2024. 3 indexed citations

Yi, Jiacai, et al.. (2022). ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images. Briefings in Bioinformatics. 23(2). 15 indexed citations

10.

Yi, Jiacai, Chengkun Wu, Xiaochen Zhang, et al.. (2022). MICER: a pre-trained encoder–decoder architecture for molecular image captioning. Bioinformatics. 38(19). 4562–4572. 11 indexed citations

11.

Zhang, Xiaochen, Chengkun Wu, Jiacai Yi, et al.. (2022). Pushing the Boundaries of Molecular Property Prediction for Drug Discovery with Multitask Learning BERT Enhanced by SMILES Enumeration. Research. 2022. 4–4. 44 indexed citations

12.

Xiong, Guo‐Li, Zhenhua Wu, Jiacai Yi, et al.. (2021). ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research. 49(W1). W5–W14. 1709 indexed citations breakdown →

13.

Wu, Chengkun, et al.. (2021). Mining microbe–disease interactions from literature via a transfer learning model. BMC Bioinformatics. 22(1). 432–432. 14 indexed citations

14.

Zhang, Xiaochen, Chengkun Wu, Zhijiang Yang, et al.. (2021). MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction. Briefings in Bioinformatics. 22(6). 104 indexed citations

15.

Xiong, Guo‐Li, Zhijiang Yang, Jiacai Yi, et al.. (2021). DDInter: an online drug–drug interaction database towards improving clinical decision-making and patient safety. Nucleic Acids Research. 50(D1). D1200–D1207. 74 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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