Thomas Blaschke
- Computational Theory and Mathematics top 0.1%
- Molecular Biology top 5%
- Materials Chemistry top 5%
- Biomedical Engineering
- Artificial Intelligence top 5%
- Co-authors
- Ola EngkvistHongming ChenMarcus OlivecronaYinhai WangJürgen BajorathJosep Arús‐PousChristian MargreitterAtanas Patronov
- Topics
- Machine Learning in Materials Science (11 papers)Computational Drug Discovery Methods (11 papers)Protein Structure and Dynamics (3 papers)
- Journals
- Journal of Medicinal ChemistryDrug Discovery TodayJournal of Chemical Information and Modeling
- Partner nations
- GermanySwedenUnited Kingdom
In The Last Decade
Thomas Blaschke
13 papers receiving 2.5k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Computational Theory and Mathematics 1.9k
- Molecular Biology 1.4k
- Materials Chemistry 1.3k
- Biomedical Engineering 228
- Artificial Intelligence 227
Countries citing papers authored by Thomas Blaschke
This map shows the geographic impact of Thomas Blaschke'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 Thomas Blaschke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Blaschke more than expected).
Fields of papers citing papers by Thomas Blaschke
This network shows the impact of papers produced by Thomas Blaschke. 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 Thomas Blaschke. The network helps show where Thomas Blaschke may publish in the future.
Co-authorship network of co-authors of Thomas Blaschke
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Blaschke. A scholar is included among the top collaborators of Thomas Blaschke 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 Thomas Blaschke. Thomas Blaschke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 19 | |
| 3 | 5 | |
| 4 | 73 | |
| 5 | REINVENT 2.0: An AI Tool for De Novo Drug Designbreakdown → | 263 |
| 6 | 4 | |
| 7 | 121 | |
| 8 | 10 | |
| 9 | 19 | |
| 10 | The rise of deep learning in drug discoverybreakdown → | 1057 |
| 11 | 22 | |
| 12 | 256 | |
| 13 | Molecular de-novo design through deep reinforcement learningbreakdown → | 781 |
About Thomas Blaschke
Thomas Blaschke is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Pharmacology, having authored 13 papers that have together received 2.6k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (11 papers), Computational Drug Discovery Methods (11 papers) and Protein Structure and Dynamics (3 papers). The work is most often cited by research in Computational Theory and Mathematics (1.9k citations), Health Informatics (50 citations) and Materials Chemistry (1.3k citations). Thomas Blaschke has collaborated with scholars based in Germany, Sweden and United Kingdom. Frequent co-authors include Ola Engkvist, Hongming Chen, Marcus Olivecrona, Yinhai Wang, Jürgen Bajorath, Josep Arús‐Pous, Christian Margreitter, Atanas Patronov, Kostas Papadopoulos and Christian Tyrchan. Their work appears in journals such as Journal of Medicinal Chemistry, Drug Discovery Today and Journal of Chemical Information and Modeling.
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.