Ola Engkvist

15.3k citations
166 papers · 8.5k indexed · 5 hit papers · h-index 43

Ola Engkvist

162 papers receiving 8.2k citations

Hit Papers

Reinvent 4: Moder...10420172026202020232505007501000

Peers

Ola Engkvist
Comparison fields: 5 of 195
  • Computational Theory and Mathematics 5.3k
  • Materials Chemistry 3.5k
  • Molecular Biology 4.2k
  • Health Informatics 72
  • Biophysics 292
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
00.5×3.4×
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

The 25 scholars most cited alongside Ola Engkvist, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ola Engkvist Line = papers co-authored together Ola Engkvist links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20256
3 20245
4 202422
5 20243
6 20249
7 20240
8 202241
9 20225
10 20227
11 202234
12 2021123
13 20202
14 2020152
15 2020102
16 202049
17 202073
18 201812
19 2017256
20 20154

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), Protein Structure and Dynamics (24 papers), Chemical Synthesis and Analysis (16 papers), Analytical Chemistry and Chromatography (12 papers), Advanced Chemical Physics Studies (12 papers), Innovative Microfluidic and Catalytic Techniques Innovation (11 papers) and Spectroscopy and Quantum Chemical Studies (11 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.

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