Mark P. Waller
Impact in
- Computational Theory and Mathematics top 0.2%
- Computational Drug Discovery Methods
- Materials Chemistry top 2%
- Machine Learning in Materials Science
Papers in
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- Protein Structure and Dynamics 8
- Chemical Synthesis and Analysis 4
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- Machine Learning in Materials Science 7
- Enzyme Structure and Function 6
- Co-authors
- Marwin Segler (3 shared papers)Mike Preuß (1 shared paper)Thierry Kogej (1 shared paper)Christian Tyrchan (1 shared paper)Michæl Bühl (6 shared papers)David E. Hibbs (9 shared papers)James A. Platts (5 shared papers)Peter A. Williams (2 shared papers)
In The Last Decade
Mark P. Waller
51 papers receiving 4.0k citations
Mark P. Waller's Hit Papers
Peers
Comparison fields: 5 of 162
- Computational Theory and Mathematics 1.7k
- Materials Chemistry 2.0k
- Physical and Theoretical Chemistry 361
- Inorganic Chemistry 356
- Health Informatics 33
Countries citing papers authored by Mark P. Waller
This map shows the geographic impact of Mark P. Waller'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 Mark P. Waller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark P. Waller more than expected).
Fields of papers citing papers by Mark P. Waller
This network shows the impact of papers produced by Mark P. Waller. 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 Mark P. Waller. The network helps show where Mark P. Waller may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark P. Waller, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Planning chemical syntheses with deep neural networks and symbolic AI Hit paper breakdown → | 2018 | 1211 |
| 2 | Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks Hit paper breakdown → | 2017 | 969 |
| 3 | Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction Hit paper breakdown → | 2017 | 368 |
| 4 | 2006 | 221 | |
| 5 | 2007 | 154 | |
| 6 | 2006 | 126 | |
| 7 | 2013 | 115 | |
| 8 | 2007 | 85 | |
| 9 | 2012 | 58 | |
| 10 | 2013 | 57 | |
| 11 | 2016 | 53 | |
| 12 | 2014 | 49 | |
| 13 | 2004 | 45 | |
| 14 | 2012 | 44 | |
| 15 | 2014 | 41 | |
| 16 | 2017 | 38 | |
| 17 | 2009 | 38 | |
| 18 | 2014 | 36 | |
| 19 | 2015 | 27 | |
| 20 | 2007 | 26 |
About Mark P. Waller
Mark P. Waller is a scholar working on Molecular Biology, Materials Chemistry, Atomic and Molecular Physics, and Optics, Physical and Theoretical Chemistry and Computational Theory and Mathematics, having authored 52 papers that have together received 4.2k indexed citations. Recurring topics across this work include Advanced Chemical Physics Studies (14 papers), Crystallography and molecular interactions (12 papers), Computational Drug Discovery Methods (9 papers), Protein Structure and Dynamics (8 papers), Machine Learning in Materials Science (7 papers), Enzyme Structure and Function (6 papers), Metal complexes synthesis and properties (5 papers) and Chemical Synthesis and Analysis (4 papers). The work is most often cited by research in Computational Theory and Mathematics (1.7k citations), Materials Chemistry (2.0k citations), Physical and Theoretical Chemistry (361 citations), Inorganic Chemistry (356 citations) and Health Informatics (33 citations). Mark P. Waller has collaborated with scholars based in Germany, China and Australia. Frequent co-authors include Marwin Segler, Mike Preuß, Thierry Kogej, Christian Tyrchan, Michæl Bühl, David E. Hibbs, James A. Platts, Peter A. Williams, Arturo Robertazzi and Jack Yang. Their work appears in journals such as Journal of Computational Chemistry, Chemistry - A European Journal, Acta Crystallographica Section D Structural Biology, Organic & Biomolecular Chemistry and The Journal of Physical Chemistry B.
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.