Piet H. van der Graaf

5.8k total citations · 1 hit paper
149 papers, 3.8k citations indexed

About

Piet H. van der Graaf is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Piet H. van der Graaf has authored 149 papers receiving a total of 3.8k indexed citations (citations by other indexed papers that have themselves been cited), including 78 papers in Molecular Biology, 32 papers in Computational Theory and Mathematics and 24 papers in Cellular and Molecular Neuroscience. Recurrent topics in Piet H. van der Graaf's work include Receptor Mechanisms and Signaling (41 papers), Computational Drug Discovery Methods (32 papers) and Pharmacogenetics and Drug Metabolism (19 papers). Piet H. van der Graaf is often cited by papers focused on Receptor Mechanisms and Signaling (41 papers), Computational Drug Discovery Methods (32 papers) and Pharmacogenetics and Drug Metabolism (19 papers). Piet H. van der Graaf collaborates with scholars based in Netherlands, United Kingdom and United States. Piet H. van der Graaf's co-authors include Meindert Danhof, Neil Benson, L. A. Peletier, Craig D. Wegner, Paul Morgan, John Arrowsmith, J. G. Coen van Hasselt, Elizabeth C. M. de Lange, Sandra A. G. Visser and Steven W. Martin and has published in prestigious journals such as Nature Communications, Nature Reviews Drug Discovery and Cancer Research.

In The Last Decade

Piet H. van der Graaf

144 papers receiving 3.7k 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.

Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival

2011 463 citations Piet H. van der Graaf et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Piet H. van der Graaf Netherlands	32	1.7k	653	518	491	455	149	3.8k
Donald E. Mager United States	39	2.1k 1.2×	535 0.8×	553 1.1×	189 0.4×	1.1k 2.3×	167	5.5k
Iftekhar Mahmood United States	31	741 0.4×	436 0.7×	1.0k 2.0×	114 0.2×	712 1.6×	168	3.3k
Johan Gabrielsson Sweden	29	964 0.6×	248 0.4×	523 1.0×	182 0.4×	344 0.8×	106	3.3k
Yasushi Okuno Japan	43	3.3k 1.9×	1.2k 1.9×	194 0.4×	201 0.4×	1.1k 2.4×	218	6.7k
Cristian Bologa United States	30	3.3k 1.9×	1.4k 2.1×	266 0.5×	459 0.9×	553 1.2×	88	5.8k
David G. Strauss United States	40	1.5k 0.9×	306 0.5×	147 0.3×	562 1.1×	160 0.4×	188	5.8k
Philippe Sanséau United Kingdom	23	3.0k 1.7×	1.3k 2.0×	248 0.5×	188 0.4×	486 1.1×	46	5.5k
Jean‐Pierre Valentin United Kingdom	39	1.8k 1.1×	493 0.8×	144 0.3×	672 1.4×	165 0.4×	198	4.2k
Tim Guilliams United Kingdom	13	2.0k 1.1×	932 1.4×	150 0.3×	266 0.5×	329 0.7×	15	4.0k
Elizabeth C. M. de Lange Netherlands	42	1.6k 0.9×	275 0.4×	419 0.8×	990 2.0×	2.1k 4.6×	155	6.2k

Countries citing papers authored by Piet H. van der Graaf

Since Specialization

Citations

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

Fields of papers citing papers by Piet H. van der Graaf

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Piet H. van der Graaf. 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 Piet H. van der Graaf. The network helps show where Piet H. van der Graaf may publish in the future.

Co-authorship network of co-authors of Piet H. van der Graaf

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

All Works

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20 of 20 papers shown

Lesko, Lawrence J. & Piet H. van der Graaf. (2025). History and Evolution of Innovations in Clinical Pharmacology. Clinical Pharmacology & Therapeutics. 118(6). 1313–1328.

Brown, Karen E., Iris K. Minichmayr, D. Max Smith, et al.. (2025). Seasons of Growth: Reflections on the CPT Editor‐in‐Training Program. Clinical Pharmacology & Therapeutics. 118(6). 1249–1252.

Šícho, Martin, Linde Schoenmaker, Marina Gorostiola González, et al.. (2024). QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool. Journal of Cheminformatics. 16(1). 128–128. 4 indexed citations

Geerts, Hugo, Piet H. van der Graaf, Edgar Schuck, et al.. (2023). A combined physiologically‐based pharmacokinetic and quantitative systems pharmacology model for modeling amyloid aggregation in Alzheimer's disease. CPT Pharmacometrics & Systems Pharmacology. 12(4). 444–461. 13 indexed citations

Geerts, Hugo, et al.. (2023). Computational neurosciences and quantitative systems pharmacology: a powerful combination for supporting drug development in neurodegenerative diseases. Journal of Pharmacokinetics and Pharmacodynamics. 51(5). 563–573. 1 indexed citations

Fu, Yu, et al.. (2023). Evaluation of a Cardiovascular Systems Model for Design and Analysis of Hemodynamic Safety Studies. Pharmaceutics. 15(4). 1175–1175.

Bakshi, Suruchi, et al.. (2023). A Review of Quantitative Systems Pharmacology Models of the Coagulation Cascade: Opportunities for Improved Usability. Pharmaceutics. 15(3). 918–918. 6 indexed citations

Kronbichler, Andreas, et al.. (2023). Using a Network-Based Analysis Approach to Investigate the Involvement of S. aureus in the Pathogenesis of Granulomatosis with Polyangiitis. International Journal of Molecular Sciences. 24(3). 1822–1822. 1 indexed citations

Guo, Tingjie, et al.. (2022). Quantitative Systems Pharmacology Modeling Framework of Autophagy in Tuberculosis: Application to Adjunctive Metformin Host-Directed Therapy. Antimicrobial Agents and Chemotherapy. 66(8). e0036622–e0036622. 1 indexed citations

10.

Wijk, Rob C. van, Dirk‐Jan van den Berg, Jeremy Liu, et al.. (2020). Anti‐tuberculosis effect of isoniazid scales accurately from zebrafish to humans. British Journal of Pharmacology. 177(24). 5518–5533. 11 indexed citations

11.

Betts, Alison, Tracey Clark, Paul Jasper, et al.. (2020). Use of translational modeling and simulation for quantitative comparison of PF-06804103, a new generation HER2 ADC, with Trastuzumab-DM1. Journal of Pharmacokinetics and Pharmacodynamics. 47(5). 513–526. 19 indexed citations

12.

Wijk, Rob C. van, Elke H. J. Krekels, Vasudev Kantae, et al.. (2019). Impact of post-hatching maturation on the pharmacokinetics of paracetamol in zebrafish larvae. Scientific Reports. 9(1). 2149–2149. 22 indexed citations

13.

Kantae, Vasudev, Elke H. J. Krekels, Anita Ordas, et al.. (2016). Pharmacokinetic Modeling of Paracetamol Uptake and Clearance in Zebrafish Larvae: Expanding the Allometric Scale in Vertebrates with Five Orders of Magnitude. Zebrafish. 13(6). 504–510. 53 indexed citations

14.

Gotta, Verena, Frank Cools, Karel Van Ammel, et al.. (2016). Application of a systems pharmacology model for translational prediction of hERG ‐mediated QT c prolongation. Pharmacology Research & Perspectives. 4(6). e00270–e00270. 12 indexed citations

15.

Hasselt, J. G. Coen van & Piet H. van der Graaf. (2015). Towards integrative systems pharmacology models in oncology drug development. Drug Discovery Today Technologies. 15. 1–8. 25 indexed citations

16.

Aston, Philip J., Gianne Derks, Balaji Agoram, & Piet H. van der Graaf. (2013). A mathematical analysis of rebound in a target-mediated drug disposition model: I.Without feedback. Journal of Mathematical Biology. 68(6). 1453–1478. 10 indexed citations

17.

Watson, Kenny J., William Gorczyca, John P. Umland, et al.. (2011). Pharmacokinetic–pharmacodynamic modelling of the effect of Moxifloxacin on QTc prolongation in telemetered cynomolgus monkeys. Journal of Pharmacological and Toxicological Methods. 63(3). 304–313. 29 indexed citations

18.

Visser, Sandra A. G., Dymphy Huntjens, Piet H. van der Graaf, L. A. Peletier, & Meindert Danhof. (2003). Mechanism-Based Modeling of the Pharmacodynamic Interaction of Alphaxalone and Midazolam in Rats. Journal of Pharmacology and Experimental Therapeutics. 307(2). 765–775. 11 indexed citations

19.

Visser, Sandra A. G., et al.. (2003). Dose-Dependent EEG Effects of Zolpidem Provide Evidence for GABAA Receptor Subtype Selectivity in Vivo. Journal of Pharmacology and Experimental Therapeutics. 304(3). 1251–1257. 20 indexed citations

20.

Zuideveld, Klaas P., et al.. (2001). A set-point model with oscillatory behavior predicts the time course of 8-OH-DPAT-induced hypothermia. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 281(6). R2059–R2071. 42 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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