Marcus Olivecrona

3.6k citations
3 papers · 2.1k indexed · 2 hit papers · h-index 3
Topics
Machine Learning in Materials Science (3 papers)Computational Drug Discovery Methods (2 papers)Modular Robots and Swarm Intelligence (1 paper)

In The Last Decade

Marcus Olivecrona

3 papers receiving 2.0k citations

Hit Papers

The rise of deep learning in drug discovery2017202620202023201820172505007501000

Peers

Marcus Olivecrona
Comparison fields: 5 of 162
  • Computational Theory and Mathematics 1.5k
  • Molecular Biology 1.1k
  • Materials Chemistry 980
  • Artificial Intelligence 203
  • Biomedical Engineering 164
Replace Thomas Blaschke with:
Thomas Blaschke Germany
Kevin Yang United States
Marwin Segler United Kingdom
Wengong Jin United States
Zhenqin Wu United States
Xutong Li China
Yinhai Wang United Kingdom
Andreas Mayr Austria
Zhaoping Xiong China
Jessica Vamathevan United Kingdom
Marcus Olivecrona relative to Thomas Blaschke Germany Thomas Blaschke's profile →
Citations per field
00.5×1.5×
Thomas Blaschke · 1×
Citations per year

Countries citing papers authored by Marcus Olivecrona

Since Specialization
Citations

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

Fields of papers citing papers by Marcus Olivecrona

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marcus Olivecrona

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

All Works

3 of 3 papers shown
#WorkIndexed citations
1
The rise of deep learning in drug discoverybreakdown →
1057
2 256
3
Molecular de-novo design through deep reinforcement learningbreakdown →
781

About Marcus Olivecrona

Marcus Olivecrona is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Artificial Intelligence, having authored 3 papers that have together received 2.1k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (3 papers), Computational Drug Discovery Methods (2 papers) and Modular Robots and Swarm Intelligence (1 paper). The work is most often cited by research in Computational Theory and Mathematics (1.5k citations), Health Informatics (48 citations) and Materials Chemistry (980 citations). Marcus Olivecrona has collaborated with scholars based in Sweden, Germany and United Kingdom. Frequent co-authors include Ola Engkvist, Hongming Chen, Thomas Blaschke, Yinhai Wang and Jürgen Bajorath. Their work appears in journals such as Drug Discovery Today, Journal of Cheminformatics and Molecular Informatics.

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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