Michael J. Keiser

8.4k total citations · 3 hit papers
51 papers, 5.5k citations indexed

About

Michael J. Keiser is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Michael J. Keiser has authored 51 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 24 papers in Computational Theory and Mathematics and 8 papers in Artificial Intelligence. Recurrent topics in Michael J. Keiser's work include Computational Drug Discovery Methods (24 papers), Receptor Mechanisms and Signaling (9 papers) and Protein Structure and Dynamics (7 papers). Michael J. Keiser is often cited by papers focused on Computational Drug Discovery Methods (24 papers), Receptor Mechanisms and Signaling (9 papers) and Protein Structure and Dynamics (7 papers). Michael J. Keiser collaborates with scholars based in United States, Switzerland and United Kingdom. Michael J. Keiser's co-authors include Brian K. Shoichet, John J. Irwin, Bryan L. Roth, Paul Ernsberger, Blaine N. Armbruster, Jérôme Hert, Christian Laggner, Kangway V. Chuang, Vincent Setola and Atheir I. Abbas and has published in prestigious journals such as Nature, Science and Nature Communications.

In The Last Decade

Michael J. Keiser

49 papers receiving 5.4k 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.

Relating protein pharmacology by ligand chemistry

2007 1.6k citations Michael J. Keiser, Bryan L. Roth et al. Nature Biotechnology profile →
Predicting new molecular targets for known drugs

2009 1.3k citations Michael J. Keiser, Vincent Setola et al. Nature profile →
Large-scale prediction and testing of drug activity on side-effect targets

2012 656 citations Michael J. Keiser, Brian K. Shoichet et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael J. Keiser United States	25	3.3k	3.1k	886	705	536	51	5.5k
Jeremy L. Jenkins United States	35	3.5k 1.0×	3.0k 1.0×	766 0.9×	568 0.8×	447 0.8×	83	5.1k
Christian Laggner Austria	31	2.6k 0.8×	1.8k 0.6×	663 0.7×	447 0.6×	656 1.2×	56	4.1k
Yvonne Light United Kingdom	16	4.4k 1.3×	3.1k 1.0×	671 0.8×	359 0.5×	527 1.0×	17	6.1k
Antti Poso Finland	41	2.7k 0.8×	1.3k 0.4×	916 1.0×	525 0.7×	866 1.6×	227	5.7k
Xiaomin Luo China	42	5.1k 1.5×	2.9k 1.0×	685 0.8×	490 0.7×	1.2k 2.3×	251	8.4k
Lei Xu China	38	4.1k 1.2×	2.0k 0.7×	605 0.7×	299 0.4×	1.2k 2.2×	223	6.8k
Mark Davies United Kingdom	18	4.4k 1.3×	4.7k 1.5×	1.0k 1.1×	537 0.8×	706 1.3×	44	7.0k
Calvin Yu‐Chian Chen Taiwan	36	2.7k 0.8×	2.0k 0.6×	529 0.6×	588 0.8×	379 0.7×	147	4.6k
Louisa J. Bellis United Kingdom	10	4.4k 1.3×	4.8k 1.6×	1000 1.1×	549 0.8×	717 1.3×	11	6.7k
Jon Chambers United Kingdom	19	4.8k 1.4×	4.3k 1.4×	1.4k 1.6×	517 0.7×	671 1.3×	22	8.4k

Countries citing papers authored by Michael J. Keiser

Since Specialization

Citations

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

Fields of papers citing papers by Michael J. Keiser

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael J. Keiser. 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 Michael J. Keiser. The network helps show where Michael J. Keiser may publish in the future.

Co-authorship network of co-authors of Michael J. Keiser

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

All Works

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20 of 20 papers shown

Skiniotis, Georgios, et al.. (2025). MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures. Nature Communications. 16(1). 6182–6182. 1 indexed citations

Keiser, Michael J., et al.. (2025). Deep learning finds convergent melanocytic morphology despite noisy archival slides. Cell Reports Methods. 5(10). 101201–101201.

Goodarzi, Hani, et al.. (2024). Learning chemical sensitivity reveals mechanisms of cellular response. Communications Biology. 7(1). 1149–1149. 2 indexed citations

Chuang, Kangway V., et al.. (2024). Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks. Journal of Chemical Information and Modeling. 64(14). 5439–5450. 5 indexed citations

Gendelev, Leo, Jack C. Taylor, Douglas Myers-Turnbull, et al.. (2024). Deep phenotypic profiling of neuroactive drugs in larval zebrafish. Nature Communications. 15(1). 9955–9955. 1 indexed citations

Keiser, Michael J., et al.. (2024). Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds. Journal of Chemical Information and Modeling. 64(19). 7398–7408. 2 indexed citations

Pearce, Thomas M., Brittany N. Dugger, Michael J. Keiser, et al.. (2023). Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles. Acta Neuropathologica Communications. 11(1). 202–202. 9 indexed citations

Keiser, Michael J., et al.. (2023). In silico discovery of repetitive elements as key sequence determinants of 3D genome folding. Cell Genomics. 3(10). 100410–100410. 8 indexed citations

Ponzoni, Luca, Jessica L. McKinley, Matthew J. O’Meara, et al.. (2022). Prioritizing Virtual Screening with Interpretable Interaction Fingerprints. Journal of Chemical Information and Modeling. 62(18). 4300–4318. 22 indexed citations

10.

Young, Albert T., Mulin Xiong, Jacob Pfau, Michael J. Keiser, & Maria L. Wei. (2020). Artificial Intelligence in Dermatology: A Primer. Journal of Investigative Dermatology. 140(8). 1504–1512. 131 indexed citations

11.

Gearing, Marla, et al.. (2020). Validation of machine learning models to detect amyloid pathologies across institutions. Acta Neuropathologica Communications. 8(1). 59–59. 22 indexed citations

12.

Chuang, Kangway V., Charles DeCarli, Lee‐Way Jin, et al.. (2019). Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline. Nature Communications. 10(1). 2173–2173. 133 indexed citations

13.

Ruderfer, Douglas M., Alexander W. Charney, Ben Readhead, et al.. (2016). Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach. The Lancet Psychiatry. 3(4). 350–357. 90 indexed citations

14.

Bruni, Giancarlo N., Andrew J. Rennekamp, Matthew N. McCarroll, et al.. (2016). Zebrafish behavioral profiling identifies multitarget antipsychotic-like compounds. Nature Chemical Biology. 12(7). 559–566. 111 indexed citations

15.

Laggner, Christian, David Kokel, Vincent Setola, et al.. (2011). Chemical informatics and target identification in a zebrafish phenotypic screen. Nature Chemical Biology. 8(2). 144–146. 92 indexed citations

16.

Yadav, Prem N., Atheir I. Abbas, Martilias S. Farrell, et al.. (2010). The Presynaptic Component of the Serotonergic System is Required for Clozapine's Efficacy. Neuropsychopharmacology. 36(3). 638–651. 51 indexed citations

17.

Keiser, Michael J., Vincent Setola, John J. Irwin, et al.. (2009). Predicting new molecular targets for known drugs. Nature. 462(7270). 175–181. 1251 indexed citations breakdown →

18.

Keiser, Michael J. & Jérôme Hert. (2009). Off-Target Networks Derived from Ligand Set Similarity. Methods in molecular biology. 575. 195–205. 19 indexed citations

19.

Keiser, Michael J., Li Basuino, Henry F. Chambers, et al.. (2009). A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism. PLoS Computational Biology. 5(8). e1000474–e1000474. 29 indexed citations

20.

Keiser, Michael J., Bryan L. Roth, Blaine N. Armbruster, et al.. (2007). Relating protein pharmacology by ligand chemistry. Nature Biotechnology. 25(2). 197–206. 1649 indexed citations breakdown →

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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