Fergus Imrie

893 total citations
17 papers, 494 citations indexed

About

Fergus Imrie is a scholar working on Computational Theory and Mathematics, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Fergus Imrie has authored 17 papers receiving a total of 494 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Theory and Mathematics, 5 papers in Molecular Biology and 5 papers in Artificial Intelligence. Recurrent topics in Fergus Imrie's work include Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Protein Structure and Dynamics (3 papers). Fergus Imrie is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Protein Structure and Dynamics (3 papers). Fergus Imrie collaborates with scholars based in United Kingdom, United States and Switzerland. Fergus Imrie's co-authors include Charlotte M. Deane, A.R. Bradley, Mihaela van der Schaar, Robert A. Davis, Willem P. van Hoorn, Eoin McKinney, Kristian Birchall, Andy Merritt, Zhaozhi Qian and Nora Pashayan and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS Medicine.

In The Last Decade

Fergus Imrie

16 papers receiving 477 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fergus Imrie United Kingdom	9	318	288	180	54	47	17	494
Lifan Chen China	10	429 1.3×	465 1.6×	183 1.0×	32 0.6×	45 1.0×	20	663
Heval Ataş Türkiye	6	317 1.0×	380 1.3×	106 0.6×	30 0.6×	33 0.7×	10	556
Tevfik Kizilören United Kingdom	3	267 0.8×	264 0.9×	81 0.5×	29 0.5×	37 0.8×	5	490
Kristina Preuer Austria	2	274 0.9×	258 0.9×	74 0.4×	39 0.7×	29 0.6×	2	384
Vladimir Aladinskiy Russia	9	431 1.4×	360 1.3×	301 1.7×	38 0.7×	33 0.7×	17	764
Daniil Polykovskiy Russia	8	327 1.0×	274 1.0×	221 1.2×	60 1.1×	17 0.4×	14	562
Kostas Papadopoulos Sweden	9	364 1.1×	315 1.1×	261 1.4×	19 0.4×	34 0.7×	10	522
Sybilla Corbett United Kingdom	3	261 0.8×	271 0.9×	82 0.5×	16 0.3×	36 0.8×	5	486
Shuting Jin China	14	357 1.1×	507 1.8×	139 0.8×	88 1.6×	23 0.5×	31	739
Hanbin Shan China	2	234 0.7×	209 0.7×	73 0.4×	28 0.5×	16 0.3×	3	426

Countries citing papers authored by Fergus Imrie

Since Specialization

Citations

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

Fields of papers citing papers by Fergus Imrie

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fergus Imrie

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

All Works

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17 of 17 papers shown

Imrie, Fergus, et al.. (2025). Automated Ensemble Multimodal Machine Learning for Healthcare. IEEE Journal of Biomedical and Health Informatics. 29(6). 4213–4226. 2 indexed citations

Deane, Charlotte M., et al.. (2025). Assessing the Chemical Intelligence of Large Language Models. Journal of Chemical Information and Modeling. 66(1). 216–227.

Imrie, Fergus, et al.. (2025). MolSnapper: Conditioning Diffusion for Structure-Based Drug Design. Journal of Chemical Information and Modeling. 65(9). 4263–4273. 4 indexed citations

Imrie, Fergus, et al.. (2024). Machine learning with requirements: A manifesto. 1. 5 indexed citations

Imrie, Fergus, et al.. (2023). Testing the limits of SMILES-based de novo molecular generation with curriculum and deep reinforcement learning. Nature Machine Intelligence. 5(4). 386–394. 28 indexed citations

Imrie, Fergus, Robert A. Davis, & Mihaela van der Schaar. (2023). Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare. Nature Machine Intelligence. 5(8). 824–829. 22 indexed citations

Callender, Thomas, Fergus Imrie, Nora Pashayan, et al.. (2023). Assessing eligibility for lung cancer screening using parsimonious ensemble machine learning models: A development and validation study. PLoS Medicine. 20(10). e1004287–e1004287. 14 indexed citations

Imrie, Fergus, et al.. (2023). AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. SHILAP Revista de lepidopterología. 2(6). e0000276–e0000276. 21 indexed citations

Imrie, Fergus, et al.. (2023). Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and Opportunities. IEEE Transactions on Artificial Intelligence. 5(6). 2589–2603. 6 indexed citations

10.

Imrie, Fergus, et al.. (2023). Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach. Anesthesia & Analgesia. 139(1). 174–185. 1 indexed citations

11.

Imrie, Fergus, et al.. (2022). Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration. Journal of Chemical Information and Modeling. 62(10). 2280–2292. 16 indexed citations

12.

Imrie, Fergus, A.R. Bradley, & Charlotte M. Deane. (2021). Generating property-matched decoy molecules using deep learning. Bioinformatics. 37(15). 2134–2141. 49 indexed citations

13.

Qin, Yuchao, et al.. (2021). Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation. Neural Information Processing Systems. 34. 1 indexed citations

14.

Imrie, Fergus, et al.. (2021). Deep generative design with 3D pharmacophoric constraints. Chemical Science. 12(43). 14577–14589. 63 indexed citations

15.

Crabbé, Jonathan, Zhaozhi Qian, Fergus Imrie, & Mihaela van der Schaar. (2021). Explaining Latent Representations with a Corpus of Examples. arXiv (Cornell University). 34. 7 indexed citations

16.

Imrie, Fergus, A.R. Bradley, Mihaela van der Schaar, & Charlotte M. Deane. (2020). Deep Generative Models for 3D Linker Design. Journal of Chemical Information and Modeling. 60(4). 1983–1995. 161 indexed citations

17.

Imrie, Fergus, A.R. Bradley, Mihaela van der Schaar, & Charlotte M. Deane. (2018). Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data. Journal of Chemical Information and Modeling. 58(11). 2319–2330. 94 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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