Ola Engkvist

15.3k citations
166 papers · 8.5k indexed · 5 hit papers · h-index 43
Topics
Computational Drug Discovery Methods (121 papers)Machine Learning in Materials Science (69 papers)Protein Structure and Dynamics (24 papers)

In The Last Decade

Ola Engkvist

162 papers receiving 8.2k citations

Hit Papers

The rise of deep learning in drug discovery2017202620202023201820172020202020242505007501000

Peers

Ola Engkvist
Comparison fields: 5 of 195
  • Computational Theory and Mathematics 5.3k
  • Molecular Biology 4.2k
  • Materials Chemistry 3.5k
  • Biomedical Engineering 696
  • Spectroscopy 630
Replace Igor V. Tetko with:
Igor V. Tetko Germany
Hongming Chen Sweden
Alexandre Varnek France
W. Patrick Walters United States
Connor W. Coley United States
Dongsheng Cao China
Luhua Lai China
Jean‐Louis Reymond Switzerland
Andreas Bender United Kingdom
Alán Aspuru‐Guzik United States
Ola Engkvist relative to Igor V. Tetko Germany Igor V. Tetko's profile →
Citations per field
00.5×2.7×
Igor V. Tetko · 1×
Citations per year

Countries citing papers authored by Ola Engkvist

Since Specialization
Citations

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

Fields of papers citing papers by Ola Engkvist

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ola Engkvist

This figure shows the co-authorship network connecting the top 25 collaborators of Ola Engkvist. A scholar is included among the top collaborators of Ola Engkvist 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 Ola Engkvist. Ola Engkvist 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 0
2 6
3 5
4 22
5 3
6 9
7 0
8 41
9 5
10 7
11 34
12 123
13 2
14 152
15 102
16 49
17 73
18 12
19 256
20 4

About Ola Engkvist

Ola Engkvist is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology, having authored 166 papers that have together received 8.5k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (121 papers), Machine Learning in Materials Science (69 papers) and Protein Structure and Dynamics (24 papers). The work is most often cited by research in Computational Theory and Mathematics (5.3k citations), Materials Chemistry (3.5k citations) and Molecular Biology (4.2k citations). Ola Engkvist has collaborated with scholars based in Sweden, United Kingdom and Germany. Frequent co-authors include Hongming Chen, Thomas Blaschke, Marcus Olivecrona, Esben Jannik Bjerrum, Yinhai Wang, Amol Thakkar, Jean‐Louis Reymond, Josep Arús‐Pous, Christian Tyrchan and Rocío Mercado. Their work appears in journals such as Chemical Reviews, Angewandte Chemie International Edition and Nature Communications.

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