Thomas Blaschke

4.6k total citations · 3 hit papers
13 papers, 2.6k citations indexed

About

Thomas Blaschke is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology. According to data from OpenAlex, Thomas Blaschke has authored 13 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computational Theory and Mathematics, 11 papers in Materials Chemistry and 8 papers in Molecular Biology. Recurrent topics in Thomas Blaschke's work include Machine Learning in Materials Science (11 papers), Computational Drug Discovery Methods (11 papers) and Protein Structure and Dynamics (3 papers). Thomas Blaschke is often cited by papers focused on Machine Learning in Materials Science (11 papers), Computational Drug Discovery Methods (11 papers) and Protein Structure and Dynamics (3 papers). Thomas Blaschke collaborates with scholars based in Germany, Sweden and United Kingdom. Thomas Blaschke's 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 and has published in prestigious journals such as Journal of Medicinal Chemistry, Drug Discovery Today and Journal of Chemical Information and Modeling.

In The Last Decade

Thomas Blaschke

13 papers receiving 2.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The rise of deep learning in drug discovery

2018 1.1k citations Hongming Chen, Ola Engkvist et al. Drug Discovery Today profile →
Molecular de-novo design through deep reinforcement learning

2017 781 citations Marcus Olivecrona, Thomas Blaschke et al. Journal of Cheminformatics profile →
REINVENT 2.0: An AI Tool for De Novo Drug Design

2020 263 citations Thomas Blaschke, Josep Arús‐Pous et al. Journal of Chemical Information and Modeling profile →

Peers

Thomas Blaschke

CTM

MB

MC

BE

AI

Kevin Yang United States

Wengong Jin United States

Marcus Olivecrona Sweden

Francesca Grisoni Italy

Christian Tyrchan Sweden

Marwin Segler United Kingdom

Kyle Swanson United States

Evan N. Feinberg United States

Esben Jannik Bjerrum Sweden

Jörg K. Wegner Belgium

Kevin Yang United States

Thomas Blaschke

1.4k ×0.7

CTM

1.2k ×0.8

MB

1.0k ×0.8

MC

240 ×1.1

BE

239 ×1.1

AI

Citations per year, relative to Thomas Blaschke Thomas Blaschke (= 1×) peers Kevin Yang

Countries citing papers authored by Thomas Blaschke

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

1.

Bjerrum, Esben Jannik, et al.. (2023). Faster and more diverse de novo molecular optimization with double-loop reinforcement learning using augmented SMILES. Journal of Computer-Aided Molecular Design. 37(8). 373–394. 10 indexed citations

2.

Blaschke, Thomas & Jürgen Bajorath. (2021). Fine-tuning of a generative neural network for designing multi-target compounds. Journal of Computer-Aided Molecular Design. 36(5). 363–371. 19 indexed citations

3.

Blaschke, Thomas & Jürgen Bajorath. (2021). Compound Dataset and Custom Code for Deep Generative multi-target Compound Design. Future Science OA. 7(6). FSO715–FSO715. 5 indexed citations

4.

Blaschke, Thomas, Ola Engkvist, Jürgen Bajorath, & Hongming Chen. (2020). Memory-assisted reinforcement learning for diverse molecular de novo design. Journal of Cheminformatics. 12(1). 68–68. 73 indexed citations

5.

Blaschke, Thomas, Josep Arús‐Pous, Hongming Chen, et al.. (2020). REINVENT 2.0: An AI Tool for De Novo Drug Design. Journal of Chemical Information and Modeling. 60(12). 5918–5922. 263 indexed citations breakdown →

6.

Blaschke, Thomas, Christian Feldmann, & Jürgen Bajorath. (2020). Prediction of Promiscuity Cliffs Using Machine Learning. Molecular Informatics. 40(1). e2000196–e2000196. 4 indexed citations

7.

Arús‐Pous, Josep, et al.. (2019). Exploring the GDB-13 chemical space using deep generative models. Journal of Cheminformatics. 11(1). 20–20. 121 indexed citations

8.

Schoeder, Clara T., et al.. (2019). Development of Chromen-4-one Derivatives as (Ant)agonists for the Lipid-Activated G Protein-Coupled Receptor GPR55 with Tunable Efficacy. ACS Omega. 4(2). 4276–4295. 10 indexed citations

9.

Blaschke, Thomas, Filip Miljković, & Jürgen Bajorath. (2019). Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis. ACS Omega. 4(4). 6883–6890. 19 indexed citations

10.

Chen, Hongming, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, & Thomas Blaschke. (2018). The rise of deep learning in drug discovery. Drug Discovery Today. 23(6). 1241–1250. 1057 indexed citations breakdown →

11.

Jasial, Swarit, Erik Gilberg, Thomas Blaschke, & Jürgen Bajorath. (2018). Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter. Journal of Medicinal Chemistry. 61(22). 10255–10264. 22 indexed citations

12.

Blaschke, Thomas, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, & Hongming Chen. (2017). Application of Generative Autoencoder in De Novo Molecular Design. Molecular Informatics. 37(1-2). 256 indexed citations

13.

Olivecrona, Marcus, Thomas Blaschke, Ola Engkvist, & Hongming Chen. (2017). Molecular de-novo design through deep reinforcement learning. Journal of Cheminformatics. 9(1). 48–48. 781 indexed citations breakdown →

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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