Connor W. Coley

16.7k citations
130 papers · 8.5k indexed · 6 hit papers · h-index 42
Topics
Machine Learning in Materials Science (77 papers)Computational Drug Discovery Methods (73 papers)Innovative Microfluidic and Catalytic Techniques Innovation (29 papers)

In The Last Decade

Connor W. Coley

123 papers receiving 8.3k citations

Hit Papers

Analyzing Learned Molecular Representations for Property ...2017202620202023201920192017201820182505007501000

Peers

Connor W. Coley
Comparison fields: 5 of 178
  • Materials Chemistry 5.1k
  • Computational Theory and Mathematics 4.4k
  • Molecular Biology 3.0k
  • Biomedical Engineering 1.8k
  • Artificial Intelligence 770
Replace Olexandr Isayev with:
Olexandr Isayev United States
Alexandre Varnek France
Ola Engkvist Sweden
Jean‐Louis Reymond Switzerland
Denis Fourches United States
Noel M. O’Boyle United Kingdom
Matthias Rupp Germany
David Rogers United States
David Weininger United States
Mark P. Waller Germany
Connor W. Coley relative to Olexandr Isayev United States Olexandr Isayev's profile →
Citations per field
00.5×1.5×1.9×
Olexandr Isayev · 1×
Citations per year

Countries citing papers authored by Connor W. Coley

Since Specialization
Citations

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

Fields of papers citing papers by Connor W. Coley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Connor W. Coley. 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 Connor W. Coley. The network helps show where Connor W. Coley may publish in the future.

Co-authorship network of co-authors of Connor W. Coley

This figure shows the co-authorship network connecting the top 25 collaborators of Connor W. Coley. A scholar is included among the top collaborators of Connor W. Coley 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 Connor W. Coley. Connor W. Coley 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
#WorkIndexed citations
1 0
2 5
3 7
4 3
5 11
6 1
7 11
8 54
9 39
10 9
11 15
12 21
13 45
14 48
15 138
16 179
17 231
18 291
19 34
20 35

About Connor W. Coley

Connor W. Coley is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Biomedical Engineering, having authored 130 papers that have together received 8.5k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (77 papers), Computational Drug Discovery Methods (73 papers) and Innovative Microfluidic and Catalytic Techniques Innovation (29 papers). The work is most often cited by research in Computational Theory and Mathematics (4.4k citations), Materials Chemistry (5.1k citations) and Molecular Biology (3.0k citations). Connor W. Coley has collaborated with scholars based in United States, China and Switzerland. Frequent co-authors include Klavs F. Jensen, William H. Green, Regina Barzilay, Tommi Jaakkola, Luke Rogers, Wengong Jin, Wenhao Gao, Hanyu Gao, Timothy F. Jamison and Milad Abolhasani. Their work appears in journals such as Nature, Science and Proceedings of the National Academy of Sciences.

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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