Matteo Manica
- Molecular Biology
- Electrical and Electronic Engineering top 10%
- Computational Theory and Mathematics top 2%
- Materials Chemistry
- Artificial Intelligence top 10%
- Co-authors
- Jannis BornMaría Rodríguez MartínezRoland MathisAlessandro CurioniHeiner GiefersManuel Le GalloCostas BekasEvangelos Eleftheriou
- Topics
- Computational Drug Discovery Methods (16 papers)Machine Learning in Materials Science (10 papers)Protein Structure and Dynamics (7 papers)
- Journals
- Nucleic Acids ResearchNature CommunicationsSHILAP Revista de lepidopterología
- Partner nations
- SwitzerlandUnited StatesUnited Kingdom
In The Last Decade
Matteo Manica
32 papers receiving 959 citations
Hit Papers
Peers
Comparison fields: 5 of 110
- Molecular Biology 328
- Electrical and Electronic Engineering 324
- Computational Theory and Mathematics 310
- Materials Chemistry 269
- Artificial Intelligence 191
Countries citing papers authored by Matteo Manica
This map shows the geographic impact of Matteo Manica'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 Manica with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matteo Manica more than expected).
Fields of papers citing papers by Matteo Manica
This network shows the impact of papers produced by Matteo Manica. 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 Manica. The network helps show where Matteo Manica may publish in the future.
Co-authorship network of co-authors of Matteo Manica
This figure shows the co-authorship network connecting the top 25 collaborators of Matteo Manica. A scholar is included among the top collaborators of Matteo Manica 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 Manica. Matteo Manica is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 20 | |
| 2 | 9 | |
| 3 | 2 | |
| 4 | 5 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 9 | |
| 8 | 21 | |
| 9 | 72 | |
| 10 | 1 | |
| 11 | 2 | |
| 12 | 2 | |
| 13 | 19 | |
| 14 | 1 | |
| 15 | 74 | |
| 16 | 38 | |
| 17 | 11 | |
| 18 | CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models | 30 |
| 19 | 3 | |
| 20 | 23 |
About Matteo Manica
Matteo Manica is a scholar working on Computational Theory and Mathematics, Health Informatics and Molecular Biology, having authored 33 papers that have together received 979 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (16 papers), Machine Learning in Materials Science (10 papers) and Protein Structure and Dynamics (7 papers). The work is most often cited by research in Health Informatics (31 citations), Computational Theory and Mathematics (310 citations) and Artificial Intelligence (191 citations). Matteo Manica has collaborated with scholars based in Switzerland, United States and United Kingdom. Frequent co-authors include Jannis Born, María Rodríguez Martínez, Roland Mathis, Alessandro Curioni, Heiner Giefers, Manuel Le Gallo, Costas Bekas, Evangelos Eleftheriou, Tomáš Tůma and Abu Sebastian. Their work appears in journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.
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.