Kieran Didi

510 total citations · 1 hit paper
7 papers, 175 citations indexed

About

Kieran Didi is a scholar working on Molecular Biology, Computational Theory and Mathematics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Kieran Didi has authored 7 papers receiving a total of 175 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Computational Theory and Mathematics and 2 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Kieran Didi's work include Glycosylation and Glycoproteins Research (2 papers), Protein Structure and Dynamics (2 papers) and Computational Drug Discovery Methods (2 papers). Kieran Didi is often cited by papers focused on Glycosylation and Glycoproteins Research (2 papers), Protein Structure and Dynamics (2 papers) and Computational Drug Discovery Methods (2 papers). Kieran Didi collaborates with scholars based in United Kingdom, Germany and United States. Kieran Didi's co-authors include Píetro Lió, Max Welling, Stéfano Ricagno, Carla P. Gomes, Michael M. Bronstein, Bruno E. Correia, Cristina Visentin, Pietro Sormanni, Tom L. Blundell and Matthew Greenig and has published in prestigious journals such as Nature Communications, Nature Methods and Frontiers in Bioengineering and Biotechnology.

In The Last Decade

Kieran Didi

5 papers receiving 169 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.

Structure-based drug design with equivariant diffusion models

2024 52 citations Arne Schneuing, Charles B. Harris et al. Nature Computational Science profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kieran Didi United Kingdom	4	120	40	39	21	16	7	175
Liwei Chang United States	9	188 1.6×	30 0.8×	39 1.0×	24 1.1×	14 0.9×	12	220
Mireia Rosell Spain	6	122 1.0×	24 0.6×	44 1.1×	19 0.9×	5 0.3×	8	149
Ameya Harmalkar United States	6	98 0.8×	36 0.9×	17 0.4×	24 1.1×	6 0.4×	9	137
Nathaniel R. Bennett United States	2	142 1.2×	43 1.1×	20 0.5×	16 0.8×	11 0.7×	2	187
Adam Moyer United States	6	126 1.1×	25 0.6×	10 0.3×	12 0.6×	8 0.5×	10	172
Mikhail Ignatov United States	9	162 1.4×	23 0.6×	38 1.0×	36 1.7×	5 0.3×	12	192
Gustaf Ahdritz United States	2	198 1.6×	15 0.4×	29 0.7×	42 2.0×	7 0.4×	2	235
Charlotte Rochereau United States	2	199 1.7×	15 0.4×	28 0.7×	44 2.1×	7 0.4×	3	237
James E. Lucas United States	6	304 2.5×	35 0.9×	36 0.9×	31 1.5×	20 1.3×	8	349

Countries citing papers authored by Kieran Didi

Since Specialization

Citations

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

Fields of papers citing papers by Kieran Didi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kieran Didi

This figure shows the co-authorship network connecting the top 25 collaborators of Kieran Didi. A scholar is included among the top collaborators of Kieran Didi 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 Kieran Didi. Kieran Didi 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.

Hu, Ying, Kieran Didi, Adam P. Cribbs, & Jianfeng Sun. (2025). Predicting PROTAC off-target effects via warhead involvement levels in drug–target interactions using graph attention neural networks. Computational and Structural Biotechnology Journal. 27. 4633–4644.

2.

Hundt, Christian, Hassan Sirelkhatim, Kieran Didi, et al.. (2025). GPU-accelerated homology search with MMseqs2. Nature Methods. 22(10). 2024–2027. 3 indexed citations

3.

Tay, Aidan P., et al.. (2024). Synsor: a tool for alignment-free detection of engineered DNA sequences. Frontiers in Bioengineering and Biotechnology. 12. 1375626–1375626.

4.

Schneuing, Arne, Charles B. Harris, Yuanqi Du, et al.. (2024). Structure-based drug design with equivariant diffusion models. Nature Computational Science. 4(12). 899–909. 52 indexed citations breakdown →

5.

Didi, Kieran, Cristina Visentin, Stéfano Ricagno, et al.. (2024). Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV. Nature Machine Intelligence. 6(1). 74–91. 40 indexed citations

6.

Siebenmorgen, Till, Filipe Menezes, Erinç Merdivan, et al.. (2024). MISATO: machine learning dataset of protein–ligand complexes for structure-based drug discovery. Nature Computational Science. 4(5). 367–378. 27 indexed citations

7.

Arter, William E., Runzhang Qi, Nadia A. Erkamp, et al.. (2022). Biomolecular condensate phase diagrams with a combinatorial microdroplet platform. Nature Communications. 13(1). 53 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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