Nathan Brown

4.3k total citations
60 papers, 2.7k citations indexed

About

Nathan Brown is a scholar working on Computational Theory and Mathematics, Molecular Biology and Pharmacology. According to data from OpenAlex, Nathan Brown has authored 60 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Computational Theory and Mathematics, 36 papers in Molecular Biology and 10 papers in Pharmacology. Recurrent topics in Nathan Brown's work include Computational Drug Discovery Methods (42 papers), Microbial Natural Products and Biosynthesis (10 papers) and Machine Learning in Materials Science (9 papers). Nathan Brown is often cited by papers focused on Computational Drug Discovery Methods (42 papers), Microbial Natural Products and Biosynthesis (10 papers) and Machine Learning in Materials Science (9 papers). Nathan Brown collaborates with scholars based in United Kingdom, Switzerland and United States. Nathan Brown's co-authors include Julian Blagg, Christos A. Nicolaou, Edgar Jacoby, Joshua Meyers, Peter Ertl, Swen Hoelder, Lewis R. Vidler, Stefan Knapp, Johann Gasteiger and Ben McKay and has published in prestigious journals such as Chemical Society Reviews, PLoS ONE and Scientific Reports.

In The Last Decade

Nathan Brown

57 papers receiving 2.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nathan Brown United Kingdom 27 1.5k 1.5k 550 428 317 60 2.7k
Michael M. Hann United Kingdom 27 2.5k 1.6× 1.5k 1.0× 927 1.7× 309 0.7× 499 1.6× 44 3.7k
Gianni Chessari United Kingdom 24 2.0k 1.3× 1.2k 0.8× 656 1.2× 462 1.1× 259 0.8× 35 2.9k
Hans Matter Germany 33 1.9k 1.2× 1.4k 1.0× 804 1.5× 531 1.2× 286 0.9× 102 3.5k
Michal Vieth United States 25 2.4k 1.6× 1.4k 1.0× 727 1.3× 439 1.0× 412 1.3× 41 3.7k
Thomas S. Rush United States 24 1.3k 0.9× 701 0.5× 330 0.6× 536 1.3× 245 0.8× 42 2.4k
Catherine E. Peishoff United States 16 2.1k 1.4× 1.6k 1.1× 608 1.1× 365 0.9× 314 1.0× 30 3.3k
Paul S. Charifson United States 29 2.9k 1.9× 1.3k 0.9× 726 1.3× 463 1.1× 357 1.1× 55 4.0k
Herman van Vlijmen Belgium 38 3.1k 2.0× 1.3k 0.9× 406 0.7× 784 1.8× 204 0.6× 98 4.3k
Paul Labute Canada 20 1.7k 1.1× 922 0.6× 485 0.9× 368 0.9× 248 0.8× 42 2.9k
Jacob D. Durrant United States 33 2.9k 1.9× 1.7k 1.2× 487 0.9× 666 1.6× 336 1.1× 78 4.2k

Countries citing papers authored by Nathan Brown

Since Specialization
Citations

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

Fields of papers citing papers by Nathan Brown

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nathan Brown

This figure shows the co-authorship network connecting the top 25 collaborators of Nathan Brown. A scholar is included among the top collaborators of Nathan Brown 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 Nathan Brown. Nathan Brown 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.
Brown, Nathan, et al.. (2024). Deep reinforcement learning for multi-criteria optimization in BIM-supported sustainable building design. International Journal of Science and Research Archive. 13(1). 1030–1048.
2.
Brown, Nathan, et al.. (2023). Efficient Transformer Knowledge Distillation: A Performance Review. 54–65. 1 indexed citations
3.
Meyers, Joshua, et al.. (2021). De novo molecular design and generative models. Drug Discovery Today. 26(11). 2707–2715. 161 indexed citations
4.
Zdrazil, Barbara, Lars Richter, Nathan Brown, & Rajarshi Guha. (2020). Moving targets in drug discovery. Scientific Reports. 10(1). 20213–20213. 21 indexed citations
5.
Neil, Daniel, et al.. (2018). Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design. International Conference on Learning Representations. 35 indexed citations
6.
Meyers, Joshua, N. Chessum, N. Yi Mok, et al.. (2018). Privileged Structures and Polypharmacology within and between Protein Families. ACS Medicinal Chemistry Letters. 9(12). 1199–1204. 12 indexed citations
7.
Brown, Nathan, Peter J. Cox, Mark Davies, et al.. (2018). Big Data in Drug Discovery. Progress in medicinal chemistry. 57(1). 277–356. 39 indexed citations
8.
Brown, Nathan. (2016). Ligand-based drug design. ˜The œbiomedical & life sciences collection.. 2016(12). e1004348–e1004348. 1 indexed citations
9.
Brown, Nathan. (2014). Bioisosteres and Scaffold Hopping in Medicinal Chemistry. Molecular Informatics. 33(6-7). 458–462. 59 indexed citations
10.
Storlie, Curtis B., et al.. (2014). Stochastic identification of malware with dynamic traces. The Annals of Applied Statistics. 8(1). 15 indexed citations
11.
Morley, Andrew, Angelo Pugliese, Kristian Birchall, et al.. (2013). Fragment-based hit identification: thinking in 3D. Drug Discovery Today. 18(23-24). 1221–1227. 129 indexed citations
12.
Westwood, Isaac M., Kathy Boxall, Nathan Brown, et al.. (2013). Fragment-Based Screening Maps Inhibitor Interactions in the ATP-Binding Site of Checkpoint Kinase 2. PLoS ONE. 8(6). e65689–e65689. 12 indexed citations
13.
Papadatos, George & Nathan Brown. (2013). In silico applications of bioisosterism in contemporary medicinal chemistry practice. Wiley Interdisciplinary Reviews Computational Molecular Science. 3(4). 339–354. 26 indexed citations
14.
Brown, Nathan. (2012). Bioisosteres in medicinal chemistry. Wiley-VCH eBooks. 18 indexed citations
15.
Lubbe, Steven, Alan Pittman, Philip Twiss, et al.. (2010). Evaluation of germline BMP4 mutation as a cause of colorectal cancer. Human Mutation. 32(1). E1928–E1938. 27 indexed citations
16.
Ertl, Peter, et al.. (2010). Bioisosteric Replacement and Scaffold Hopping in Lead Generation and Optimization. Molecular Informatics. 29(5). 366–385. 162 indexed citations
17.
Jacoby, Edgar, Andreas Boettcher, Lorenz M. Mayr, et al.. (2009). Knowledge-Based Virtual Screening: Application to the MDM4/p53 Protein–Protein Interaction. Methods in molecular biology. 575. 173–194. 17 indexed citations
18.
Brown, Nathan & Edgar Jacoby. (2006). On Scaffolds and Hopping in Medicinal Chemistry. Mini-Reviews in Medicinal Chemistry. 6(11). 1217–1229. 122 indexed citations
19.
Brown, Nathan, Ben McKay, & Johann Gasteiger. (2006). A novel workflow for the inverse QSPR problem using multiobjective optimization. Journal of Computer-Aided Molecular Design. 20(5). 333–341. 36 indexed citations
20.
Brown, Nathan, Ben McKay, & Johann Gasteiger. (2004). The de novo design of median molecules within a property range of interest. Journal of Computer-Aided Molecular Design. 18(12). 761–771. 25 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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