Mark P. Waller

51 papers receiving 4.0k citations

Hit Papers

Planning chemical syntheses with deep neural networks and symbolic AI 2018 · 1.2k citations
1.2k20172026202020234008001.2k

Peers

Mark P. Waller
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
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Teodoro Laino Switzerland
Gregory A. Landrum Switzerland
U. Deva Priyakumar India
Connor W. Coley United States
Benjamín Sánchez-Lengeling United States
Rafael Gómez‐Bombarelli United States
Sereina Riniker Switzerland
W. Patrick Walters United States
Thierry Kogej Sweden
Marwin Segler United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Mark P. Waller

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network

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.

Border = papers with Mark P. Waller Line = papers co-authored together Mark P. Waller links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20252
2 20232
3 20208
4 201912
5
Planning chemical syntheses with deep neural networks and symbolic AI
Hit paper breakdown →
20181211
6 201718
7 201738
8 201622
9 201527
10 201521
11 20155
12 201357
13 2013115
14 201216
15 201244
16 201258
17 2006126
18 2006221
19 200517
20 200319

About Mark P. Waller

Mark P. Waller is a scholar working on Physical and Theoretical Chemistry, Structural Biology, Computational Theory and Mathematics, Atomic and Molecular Physics, and Optics and Inorganic Chemistry, 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 Spectroscopy and Quantum Chemical Studies (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, Christian Tyrchan, Thierry Kogej, 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.

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