Alberto Manganaro

3.0k citations
41 papers · 1.2k indexed · h-index 20
Topics
Computational Drug Discovery Methods (28 papers)Effects and risks of endocrine disrupting chemicals (6 papers)Machine Learning in Materials Science (6 papers)
Partner nations
ItalyJapanGermany

In The Last Decade

Alberto Manganaro

41 papers receiving 1.2k citations

Peers

Alberto Manganaro
Comparison fields: 5 of 112
  • Computational Theory and Mathematics 607
  • Health, Toxicology and Mutagenesis 337
  • Molecular Biology 253
  • Pollution 192
  • Environmental Chemistry 153
Replace Simona Kovarich with:
Simona Kovarich Italy
Domenico Gadaleta Italy
Alessandra Roncaglioni Italy
Anna Lombardo Italy
Todor Pavlov Bulgaria
Manuela Pavan Italy
Cecilia Bossa Italy
Alessandro Sangion Canada
Thomas Steger‐Hartmann Germany
Tatiana I. Netzeva United Kingdom
Alberto Manganaro relative to Simona Kovarich Italy Simona Kovarich's profile →
Citations per field
00.5×1.5×
Simona Kovarich · 1×
Citations per year

Countries citing papers authored by Alberto Manganaro

Since Specialization
Citations

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

Fields of papers citing papers by Alberto Manganaro

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alberto Manganaro

This figure shows the co-authorship network connecting the top 25 collaborators of Alberto Manganaro. A scholar is included among the top collaborators of Alberto Manganaro 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 Alberto Manganaro. Alberto Manganaro 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 1
2 1
3 1
4 15
5 10
6 12
7 24
8 22
9 25
10 34
11 107
12 28
13 34
14 70
15 96
16 18
17 16
18 9
19 7
20
The DART (Decision Analysis by Ranking Techniques) software
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About Alberto Manganaro

Alberto Manganaro is a scholar working on Computational Theory and Mathematics, Chemical Health and Safety and Health, Toxicology and Mutagenesis, having authored 41 papers that have together received 1.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (28 papers), Effects and risks of endocrine disrupting chemicals (6 papers) and Machine Learning in Materials Science (6 papers). The work is most often cited by research in Computational Theory and Mathematics (607 citations), Health, Toxicology and Mutagenesis (337 citations) and Pollution (192 citations). Alberto Manganaro has collaborated with scholars based in Italy, Japan and Germany. Frequent co-authors include Emilio Benfenati, Giuseppina Gini, Anna Lombardo, Alessandra Roncaglioni, Fabiola Pizzo, Serena Manganelli, Andrey A. Toropov, Alla P. Toropova, Alessandro Esposito and Orazio Nicolotti. Their work appears in journals such as The Science of The Total Environment, Chemosphere and International Journal of Molecular 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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