Daniel P. Russo

2.4k citations
36 papers · 1.5k indexed · 1 hit paper · h-index 20
Topics
Computational Drug Discovery Methods (26 papers)Metabolomics and Mass Spectrometry Studies (7 papers)Pharmacogenetics and Drug Metabolism (7 papers)

In The Last Decade

Daniel P. Russo

35 papers receiving 1.5k citations

Hit Papers

Exploiting machine learning for end-to-end drug discovery...20192026202120232019100200300

Peers

Daniel P. Russo
Comparison fields: 5 of 153
  • Computational Theory and Mathematics 733
  • Molecular Biology 509
  • Materials Chemistry 378
  • Biomedical Engineering 159
  • Pharmacology 104
Replace Kimberley M. Zorn with:
Kimberley M. Zorn United States
Thomas R. Lane United States
Valery Tkachenko United States
Jessica Vamathevan United Kingdom
Kevin Yang United States
Stephen J. Capuzzi United States
Angela Serra Finland
Shaun M. Kandathil United Kingdom
Joe G. Greener United Kingdom
Daniel P. Russo relative to Kimberley M. Zorn United States Kimberley M. Zorn's profile →
Citations per field
00.5×3.7×
Kimberley M. Zorn · 1×
Citations per year

Countries citing papers authored by Daniel P. Russo

Since Specialization
Citations

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

Fields of papers citing papers by Daniel P. Russo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel P. Russo. 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 Daniel P. Russo. The network helps show where Daniel P. Russo may publish in the future.

Co-authorship network of co-authors of Daniel P. Russo

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 4
2 16
3 2
4 5
5 30
6 9
7 26
8 14
9 44
10 23
11 20
12 29
13
Exploiting machine learning for end-to-end drug discovery and developmentbreakdown →
352
14 19
15 58
16 50
17 2
18 2
19 4
20 25

About Daniel P. Russo

Daniel P. Russo is a scholar working on Computational Theory and Mathematics, Pharmacology and Small Animals, having authored 36 papers that have together received 1.5k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (26 papers), Metabolomics and Mass Spectrometry Studies (7 papers) and Pharmacogenetics and Drug Metabolism (7 papers). The work is most often cited by research in Computational Theory and Mathematics (733 citations), Health Informatics (31 citations) and Biophysics (70 citations). Daniel P. Russo has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Sean Ekins, Hao Zhu, Kimberley M. Zorn, Alex M. Clark, Valery Tkachenko, Alexandru Korotcov, Thomas R. Lane, Ana C. Puhl, Jennifer J. Klein and Anthony J. Hickey. Their work appears in journals such as Nature Materials, Nano Letters and Environmental Science & Technology.

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026