Andrea Cavalli

19.1k total citations · 2 hit papers
309 papers, 14.9k citations indexed

About

Andrea Cavalli is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Andrea Cavalli has authored 309 papers receiving a total of 14.9k indexed citations (citations by other indexed papers that have themselves been cited), including 170 papers in Molecular Biology, 86 papers in Computational Theory and Mathematics and 81 papers in Pharmacology. Recurrent topics in Andrea Cavalli's work include Computational Drug Discovery Methods (85 papers), Protein Structure and Dynamics (55 papers) and Cholinesterase and Neurodegenerative Diseases (55 papers). Andrea Cavalli is often cited by papers focused on Computational Drug Discovery Methods (85 papers), Protein Structure and Dynamics (55 papers) and Cholinesterase and Neurodegenerative Diseases (55 papers). Andrea Cavalli collaborates with scholars based in Italy, Switzerland and United States. Andrea Cavalli's co-authors include Maurizio Recanatini, María Laura Bolognesi, Matteo Masetti, Giovanni Bottegoni, Marco De Vivo, Carlo Melchiorre, Vincenza Andrisano, Michela Rosini, Manuela Bartolini and Sergio Decherchi and has published in prestigious journals such as Chemical Reviews, Proceedings of the National Academy of Sciences and Journal of the American Chemical Society.

In The Last Decade

Andrea Cavalli

301 papers receiving 14.7k citations

Hit Papers

Multi-target-Directed Ligands To Combat Neurodegenerative... 2008 2026 2014 2020 2008 2016 250 500 750

Peers

Andrea Cavalli
Oleg Trott United States
Michel F. Sanner United States
William Lindstrom United States
Vincent Zoete Switzerland
Tudor I. Oprea United States
Andrea Cavalli
Citations per year, relative to Andrea Cavalli Andrea Cavalli (= 1×) peers Hualiang Jiang

Countries citing papers authored by Andrea Cavalli

Since Specialization
Citations

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

Fields of papers citing papers by Andrea Cavalli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrea Cavalli

This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Cavalli. A scholar is included among the top collaborators of Andrea Cavalli 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 Andrea Cavalli. Andrea Cavalli 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
1.
Massenzio, Francesca, José Antonio Ortega, Debora Russo, et al.. (2025). Exploration of the Neuromodulatory Properties of Fyn and GSK-3β Kinases Exploiting 7-Azaindole-Based Inhibitors. Journal of Medicinal Chemistry. 68(16). 17130–17154. 1 indexed citations
2.
Previtali, Viola, et al.. (2023). Novel Insights into RAD52’s Structure, Function, and Druggability for Synthetic Lethality and Innovative Anticancer Therapies. Cancers. 15(6). 1817–1817. 6 indexed citations
3.
Catalano, Federico, Roberto Marotta, Samuel H. Myers, et al.. (2023). Isolation and Characterization of Monomeric Human RAD51: A Novel Tool for Investigating Homologous Recombination in Cancer. Angewandte Chemie. 135(51).
4.
Spyrakis, Francesca, et al.. (2023). Molecular Dynamics and Machine Learning Give Insights on the Flexibility–Activity Relationships in Tyrosine Kinome. Journal of Chemical Information and Modeling. 63(15). 4814–4826. 2 indexed citations
5.
Origlia, Nicola, Francesca Musumeci, Silvia Schenone, et al.. (2023). New Insights into the LANCL2-ABA Binding Mode towards the Evaluation of New LANCL Agonists. Pharmaceutics. 15(12). 2754–2754. 3 indexed citations
6.
Sondo, Elvira, Emanuela Pesce, Valeria Tomati, et al.. (2023). Innovative Strategy toward Mutant CFTR Rescue in Cystic Fibrosis: Design and Synthesis of Thiadiazole Inhibitors of the E3 Ligase RNF5. Journal of Medicinal Chemistry. 66(14). 9797–9822. 10 indexed citations
7.
Manerba, Marcella, Roberto Marotta, Arianna Gennari, et al.. (2022). The Mechanistic Understanding of RAD51 Defibrillation: A Critical Step in BRCA2-Mediated DNA Repair by Homologous Recombination. International Journal of Molecular Sciences. 23(15). 8338–8338. 7 indexed citations
8.
Uva, Paolo, Tommaso Pippucci, Marco Seri, et al.. (2022). Exploration of Tools for the Interpretation of Human Non-Coding Variants. International Journal of Molecular Sciences. 23(21). 12977–12977. 11 indexed citations
9.
Cavalli, Andrea, et al.. (2021). Computational analysis of the effect of [Tea][Ms] and [Tea][H 2 PO 4 ] ionic liquids on the structure and stability of Aβ(17–42) amyloid fibrils. Physical Chemistry Chemical Physics. 23(11). 6695–6709. 7 indexed citations
10.
Barone, Monica, Simone Rampelli, Elena Biagi, et al.. (2021). Searching for New Microbiome-Targeted Therapeutics through a Drug Repurposing Approach. Journal of Medicinal Chemistry. 64(23). 17277–17286. 6 indexed citations
11.
Decherchi, Sergio, et al.. (2021). Machine Learning and Enhanced Sampling Simulations for Computing the Potential of Mean Force and Standard Binding Free Energy. Journal of Chemical Theory and Computation. 17(8). 5287–5300. 35 indexed citations
12.
Protti, Michele, et al.. (2020). Assessment of capillary volumetric blood microsampling for the analysis of central nervous system drugs and metabolites. The Analyst. 145(17). 5744–5753. 26 indexed citations
13.
Ballone, P., et al.. (2020). Solubility Advantage of Amorphous Ketoprofen. Thermodynamic and Kinetic Aspects by Molecular Dynamics and Free Energy Approaches. Journal of Chemical Theory and Computation. 16(7). 4126–4140. 12 indexed citations
14.
15.
Martino, Rita Maria Concetta Di, Giovanni Bottegoni, Francesca Seghetti, et al.. (2020). Multitarget Compounds for Bipolar Disorder: From Rational Design to Preliminary Pharmacokinetic Evaluation. ChemMedChem. 15(11). 949–954. 4 indexed citations
16.
Ferraro, Mariarosaria, et al.. (2019). Multi-target dopamine D3 receptor modulators: Actionable knowledge for drug design from molecular dynamics and machine learning. European Journal of Medicinal Chemistry. 188. 111975–111975. 14 indexed citations
17.
Decherchi, Sergio, Giovanni Bottegoni, Andrea Spitaleri, Walter Rocchia, & Andrea Cavalli. (2018). BiKi Life Sciences: A New Suite for Molecular Dynamics and Related Methods in Drug Discovery. Journal of Chemical Information and Modeling. 58(2). 219–224. 46 indexed citations
18.
Schiebel, J., Roberto Gaspari, Hans‐Dieter Gerber, et al.. (2017). Charges Shift Protonation: Neutron Diffraction Reveals that Aniline and 2‐Aminopyridine Become Protonated Upon Binding to Trypsin. Angewandte Chemie International Edition. 56(17). 4887–4890. 23 indexed citations
19.
Uliassi, Elisa, Giulia Fiorani, R. Luise Krauth‐Siegel, et al.. (2017). Crassiflorone derivatives that inhibit Trypanosoma brucei glyceraldehyde-3-phosphate dehydrogenase ( Tb GAPDH) and Trypanosoma cruzi trypanothione reductase ( Tc TR) and display trypanocidal activity. European Journal of Medicinal Chemistry. 141. 138–148. 25 indexed citations
20.
Prati, Federica, Giovanni Bottegoni, María Laura Bolognesi, & Andrea Cavalli. (2017). BACE-1 Inhibitors: From Recent Single-Target Molecules to Multitarget Compounds for Alzheimer’s Disease. Journal of Medicinal Chemistry. 61(3). 619–637. 104 indexed citations

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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