Matteo Aldeghi
- Molecular Biology top 5%
- Materials Chemistry top 5%
- Computational Theory and Mathematics top 0.5%
- Organic Chemistry top 5%
- Biomedical Engineering top 10%
- Co-authors
- Alán Aspuru‐GuzikPhilip C. BigginBert L. de GrootStefan KnappMichael J. BodkinAlexander HeifetzVytautas GapsysHuachen Tao
- Topics
- Computational Drug Discovery Methods (19 papers)Machine Learning in Materials Science (12 papers)Protein Structure and Dynamics (10 papers)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Matteo Aldeghi
39 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 164
- Molecular Biology 1.5k
- Materials Chemistry 971
- Computational Theory and Mathematics 925
- Organic Chemistry 409
- Biomedical Engineering 407
Countries citing papers authored by Matteo Aldeghi
This map shows the geographic impact of Matteo Aldeghi'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 Matteo Aldeghi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matteo Aldeghi more than expected).
Fields of papers citing papers by Matteo Aldeghi
This network shows the impact of papers produced by Matteo Aldeghi. 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 Matteo Aldeghi. The network helps show where Matteo Aldeghi may publish in the future.
Co-authorship network of co-authors of Matteo Aldeghi
This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Aldeghi. A scholar is included among the top collaborators of Matteo Aldeghi 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 Matteo Aldeghi. Matteo Aldeghi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 13 | |
| 2 | 6 | |
| 3 | 3 | |
| 4 | 6 | |
| 5 | Machine learning models to accelerate the design of polymeric long-acting injectablesbreakdown → | 127 |
| 6 | On scientific understanding with artificial intelligencebreakdown → | 186 |
| 7 | 28 | |
| 8 | 182 | |
| 9 | 74 | |
| 10 | 18 | |
| 11 | 82 | |
| 12 | 17 | |
| 13 | 100 | |
| 14 | 14 | |
| 15 | 47 | |
| 16 | 32 | |
| 17 | 45 | |
| 18 | 14 | |
| 19 | 4 | |
| 20 | 255 |
About Matteo Aldeghi
Matteo Aldeghi is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology, having authored 40 papers that have together received 3.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (19 papers), Machine Learning in Materials Science (12 papers) and Protein Structure and Dynamics (10 papers). The work is most often cited by research in Computational Theory and Mathematics (925 citations), Molecular Biology (1.5k citations) and Materials Chemistry (971 citations). Matteo Aldeghi has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Alán Aspuru‐Guzik, Philip C. Biggin, Bert L. de Groot, Stefan Knapp, Michael J. Bodkin, Alexander Heifetz, Vytautas Gapsys, Huachen Tao, Tianyi Wu and Eugenia Kumacheva. Their work appears in journals such as Journal of the American Chemical Society, Nature Communications and Accounts of Chemical Research.
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.