Daniel Probst

3.1k total citations · 1 hit paper
56 papers, 1.8k citations indexed

About

Daniel Probst is a scholar working on Fluid Flow and Transfer Processes, Computational Theory and Mathematics and Computational Mechanics. According to data from OpenAlex, Daniel Probst has authored 56 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Fluid Flow and Transfer Processes, 21 papers in Computational Theory and Mathematics and 19 papers in Computational Mechanics. Recurrent topics in Daniel Probst's work include Advanced Combustion Engine Technologies (22 papers), Computational Drug Discovery Methods (20 papers) and Combustion and flame dynamics (18 papers). Daniel Probst is often cited by papers focused on Advanced Combustion Engine Technologies (22 papers), Computational Drug Discovery Methods (20 papers) and Combustion and flame dynamics (18 papers). Daniel Probst collaborates with scholars based in Switzerland, United States and Austria. Daniel Probst's co-authors include Jean‐Louis Reymond, Alice Capecchi, Philippe Schwaller, Teodoro Laino, Mahendra Awale, Alain C. Vaucher, Vishnu H Nair, Sibendu Som, David Kreutter and Yuanjiang Pei and has published in prestigious journals such as Nature Communications, Bioinformatics and Cardiovascular Research.

In The Last Decade

Daniel Probst

53 papers receiving 1.8k citations

Hit Papers

One molecular fingerprint to rule them all: drugs, biomol... 2020 2026 2022 2024 2020 50 100 150 200
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.

  1. One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome
    2020 223 citations Alice Capecchi, Daniel Probst et al. Journal of Cheminformatics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel Probst Switzerland 22 815 791 499 268 227 56 1.8k
Thomas Seidel Austria 15 513 0.6× 563 0.7× 260 0.5× 25 0.1× 12 0.1× 53 1.0k
Yufeng Liu China 31 1.3k 1.6× 39 0.0× 218 0.4× 50 0.2× 92 0.4× 119 2.6k
Payel Das United States 31 1.2k 1.5× 405 0.5× 718 1.4× 11 0.0× 27 0.1× 86 2.3k
Xinglong Zhang China 13 315 0.4× 196 0.2× 124 0.2× 21 0.1× 14 0.1× 67 1.2k
Zhi‐An Wang China 37 1.5k 1.9× 620 0.8× 82 0.2× 19 0.1× 41 0.2× 150 4.0k
Zhihua Gan China 37 494 0.6× 827 1.0× 123 0.2× 7 0.0× 335 1.5× 92 5.0k
Satoru Goto Japan 24 469 0.6× 60 0.1× 312 0.6× 29 0.1× 21 0.1× 215 2.6k
Sung‐Sau So United States 20 594 0.7× 771 1.0× 197 0.4× 8 0.0× 13 0.1× 34 1.8k
Muhammad Kamran Jamil Pakistan 27 58 0.1× 791 1.0× 105 0.2× 290 1.1× 521 2.3× 143 2.3k

Countries citing papers authored by Daniel Probst

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Probst

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Probst

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Probst. A scholar is included among the top collaborators of Daniel Probst 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 Daniel Probst. Daniel Probst 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
1.
Vandergheynst, Pierre, et al.. (2024). Molecular set representation learning. Nature Machine Intelligence. 6(7). 754–763. 14 indexed citations
2.
Manica, Matteo, et al.. (2024). Language models can identify enzymatic binding sites in protein sequences. Computational and Structural Biotechnology Journal. 23. 1929–1937. 9 indexed citations
3.
Probst, Daniel, et al.. (2023). Alchemical analysis of FDA approved drugs. Digital Discovery. 2(5). 1289–1296. 6 indexed citations
4.
Unsleber, Jan P., Alain C. Vaucher, Thomas Weymuth, et al.. (2023). Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning. Digital Discovery. 2(3). 663–673. 7 indexed citations
5.
Probst, Daniel. (2023). An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification. Journal of Cheminformatics. 15(1). 113–113. 4 indexed citations
6.
Probst, Daniel. (2023). Aiming beyond slight increases in accuracy. Nature Reviews Chemistry. 7(4). 227–228. 9 indexed citations
7.
Castrogiovanni, Alessandro, Théophile Gaudin, Teodoro Laino, et al.. (2023). Fuelling the Digital Chemistry Revolution with Language Models. CHIMIA International Journal for Chemistry. 77(7/8). 484–484. 1 indexed citations
8.
Probst, Daniel, et al.. (2022). Biocatalysed synthesis planning using data-driven learning. Nature Communications. 13(1). 964–964. 74 indexed citations
9.
Capecchi, Alice, Daniel Probst, & Jean‐Louis Reymond. (2020). One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. Journal of Cheminformatics. 12(1). 43–43. 223 indexed citations breakdown →
10.
Probst, Daniel, et al.. (2020). The name tells the story: Two-pore channels. Cell Calcium. 89. 102215–102215. 3 indexed citations
11.
Probst, Daniel & Jean‐Louis Reymond. (2020). Visualization of very large high-dimensional data sets as minimum spanning trees. Journal of Cheminformatics. 12(1). 12–12. 193 indexed citations
12.
Pei, Yuanjiang, Pinaki Pal, Yu Zhang, et al.. (2019). CFD-Guided Combustion System Optimization of a Gasoline Range Fuel in a Heavy-Duty Compression Ignition Engine Using Automatic Piston Geometry Generation and a Supercomputer. SAE International Journal of Advances and Current Practices in Mobility. 1(1). 166–179. 39 indexed citations
13.
Arús‐Pous, Josep, Mahendra Awale, Daniel Probst, & Jean‐Louis Reymond. (2019). Exploring Chemical Space with Machine Learning. CHIMIA International Journal for Chemistry. 73(12). 1018–1018. 23 indexed citations
14.
Awale, Mahendra, Daniel Probst, Lijo Cherian Ozhathil, et al.. (2019). Optimizing TRPM4 inhibitors in the MHFP6 chemical space. European Journal of Medicinal Chemistry. 166. 167–177. 16 indexed citations
15.
Probst, Daniel & Jean‐Louis Reymond. (2018). A probabilistic molecular fingerprint for big data settings. Journal of Cheminformatics. 10(1). 66–66. 102 indexed citations
16.
Arús‐Pous, Josep, Daniel Probst, & Jean‐Louis Reymond. (2018). Deep Learning Invades Drug Design and Synthesis. CHIMIA International Journal for Chemistry. 72(1-2). 70–70. 5 indexed citations
17.
Pal, Pinaki, Daniel Probst, Yuanjiang Pei, et al.. (2017). Numerical Investigation of a Gasoline-Like Fuel in a Heavy-Duty Compression Ignition Engine Using Global Sensitivity Analysis. 10(1).
18.
Jin, Xian, Ricardo Visini, Daniel Probst, et al.. (2017). Chemical space guided discovery of antimicrobial bridged bicyclic peptides against Pseudomonas aeruginosa and its biofilms. Chemical Science. 8(10). 6784–6798. 46 indexed citations
19.
Awale, Mahendra, Ricardo Visini, Daniel Probst, Josep Arús‐Pous, & Jean‐Louis Reymond. (2017). Chemical Space: Big Data Challenge for Molecular Diversity. CHIMIA International Journal for Chemistry. 71(10). 661–661. 41 indexed citations
20.
Eder, Petra, Daniel Probst, Christian Rosker, et al.. (2006). Phospholipase C-dependent control of cardiac calcium homeostasis involves a TRPC3-NCX1 signaling complex. Cardiovascular Research. 73(1). 111–119. 79 indexed citations

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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