Matteo Aldeghi

5.0k total citations · 4 hit papers
40 papers, 3.2k citations indexed

About

Matteo Aldeghi is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Matteo Aldeghi has authored 40 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 20 papers in Computational Theory and Mathematics and 17 papers in Materials Chemistry. Recurrent topics in Matteo Aldeghi's work include Computational Drug Discovery Methods (19 papers), Machine Learning in Materials Science (12 papers) and Protein Structure and Dynamics (10 papers). Matteo Aldeghi is often cited by papers focused on Computational Drug Discovery Methods (19 papers), Machine Learning in Materials Science (12 papers) and Protein Structure and Dynamics (10 papers). Matteo Aldeghi collaborates with scholars based in Germany, United Kingdom and United States. Matteo Aldeghi's 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 and has published in prestigious journals such as Journal of the American Chemical Society, Nature Communications and Accounts of Chemical Research.

In The Last Decade

Matteo Aldeghi

39 papers receiving 3.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Nanoparticle synthesis assisted by machine learning

2021 378 citations Matteo Aldeghi, Alán Aspuru‐Guzik et al. profile →
Data-Driven Strategies for Accelerated Materials Design

2021 299 citations Robert Pollice, Gabriel dos Passos Gomes et al. profile →
On scientific understanding with artificial intelligence

2022 186 citations Mario Krenn, Robert Pollice et al. Nature Reviews Physics profile →
Machine learning models to accelerate the design of polymeric long-acting injectables

2023 127 citations Pauric Bannigan, Zeqing Bao et al. Nature Communications profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Matteo Aldeghi Germany	25	1.5k	971	925	409	407	40	3.2k
U. Deva Priyakumar India	33	1.6k 1.1×	1.1k 1.2×	723 0.8×	983 2.4×	217 0.5×	151	3.6k
Mark P. Waller Germany	23	1.7k 1.1×	2.0k 2.1×	1.7k 1.8×	650 1.6×	437 1.1×	52	4.2k
Gregory A. Landrum Switzerland	32	1.7k 1.2×	1.6k 1.7×	1.9k 2.1×	693 1.7×	240 0.6×	69	4.6k
Xiaohong Liu China	35	1.9k 1.3×	1.6k 1.6×	1.2k 1.4×	459 1.1×	587 1.4×	119	4.9k
Paul Czodrowski Germany	21	2.5k 1.7×	738 0.8×	1.3k 1.4×	318 0.8×	249 0.6×	41	4.2k
Vishwesh Venkatraman Norway	30	1.1k 0.7×	738 0.8×	641 0.7×	247 0.6×	290 0.7×	65	2.7k
Lorenz C. Blum Switzerland	18	1.5k 1.0×	1.1k 1.1×	1.6k 1.7×	271 0.7×	167 0.4×	18	2.9k
Payel Das United States	31	1.2k 0.8×	718 0.7×	405 0.4×	173 0.4×	235 0.6×	86	2.3k
Diwakar Shukla United States	32	2.9k 1.9×	913 0.9×	405 0.4×	190 0.5×	343 0.8×	129	4.4k
Yu‐Ting Lin Taiwan	26	1.1k 0.7×	1.1k 1.2×	1.1k 1.2×	387 0.9×	328 0.8×	105	4.0k

Countries citing papers authored by Matteo Aldeghi

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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20 of 20 papers shown

Hickman, Riley J., Malcolm Sim, Sergio Pablo‐García, et al.. (2025). Atlas: a brain for self-driving laboratories. Digital Discovery. 4(4). 1006–1029. 13 indexed citations

Ash, Jeremy R., Raquel Rodríguez-Pérez, Matteo Aldeghi, et al.. (2025). Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery. Journal of Chemical Information and Modeling. 65(18). 9398–9411. 6 indexed citations

Singh, Sukrit, Vytautas Gapsys, Matteo Aldeghi, et al.. (2025). Prospective Evaluation of Structure-Based Simulations Reveal Their Ability to Predict the Impact of Kinase Mutations on Inhibitor Binding. The Journal of Physical Chemistry B. 129(11). 2882–2902. 3 indexed citations

Ash, Jeremy R., Matteo Aldeghi, Raquel Rodríguez-Pérez, et al.. (2024). A call for an industry-led initiative to critically assess machine learning for real-world drug discovery. Nature Machine Intelligence. 6(10). 1120–1121. 6 indexed citations

Bannigan, Pauric, Zeqing Bao, Riley J. Hickman, et al.. (2023). Machine learning models to accelerate the design of polymeric long-acting injectables. Nature Communications. 14(1). 35–35. 127 indexed citations breakdown →

Krenn, Mario, Robert Pollice, Si Yue Guo, et al.. (2022). On scientific understanding with artificial intelligence. Nature Reviews Physics. 4(12). 761–769. 186 indexed citations breakdown →

Aldeghi, Matteo, David Graff, Nathan C. Frey, et al.. (2022). Roughness of Molecular Property Landscapes and Its Impact on Modellability. Journal of Chemical Information and Modeling. 62(19). 4660–4671. 28 indexed citations

Bannigan, Pauric, Matteo Aldeghi, Zeqing Bao, et al.. (2021). Machine learning directed drug formulation development. Advanced Drug Delivery Reviews. 175. 113806–113806. 182 indexed citations

Gapsys, Vytautas, Ahmet Yıldırım, Matteo Aldeghi, et al.. (2021). Accurate absolute free energies for ligand–protein binding based on non-equilibrium approaches. Communications Chemistry. 4(1). 61–61. 74 indexed citations

10.

Pappenheim, Fabian Rabe von, et al.. (2020). Structural basis for antibiotic action of the B1 antivitamin 2′-methoxy-thiamine. Nature Chemical Biology. 16(11). 1237–1245. 18 indexed citations

11.

Rizzi, Andrea, David R. Slochower, Matteo Aldeghi, et al.. (2020). The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations. Journal of Computer-Aided Molecular Design. 34(5). 601–633. 82 indexed citations

12.

Heifetz, Alexander, Iñaki Morao, M. Madan Babu, et al.. (2020). Characterizing Interhelical Interactions of G-Protein Coupled Receptors with the Fragment Molecular Orbital Method. Journal of Chemical Theory and Computation. 16(4). 2814–2824. 17 indexed citations

13.

Aldeghi, Matteo, Matthew P. Blakeley, Andreas Ostermann, et al.. (2019). A molecular mechanism for transthyretin amyloidogenesis. Nature Communications. 10(1). 925–925. 100 indexed citations

14.

Heifetz, Alexander, Michelle Southey, Iñaki Morao, et al.. (2019). Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach. Current Opinion in Structural Biology. 55. 85–92. 14 indexed citations

15.

Aldeghi, Matteo, Gregory A. Ross, Michael J. Bodkin, et al.. (2018). Large-scale analysis of water stability in bromodomain binding pockets with grand canonical Monte Carlo. Communications Chemistry. 1(1). 47 indexed citations

16.

Aldeghi, Matteo, Bert L. de Groot, & Vytautas Gapsys. (2018). Accurate Calculation of Free Energy Changes upon Amino Acid Mutation. Methods in molecular biology. 1851. 19–47. 32 indexed citations

17.

Aldeghi, Matteo, et al.. (2018). Absolute Alchemical Free Energy Calculations for Ligand Binding: A Beginner’s Guide. Methods in molecular biology. 1762. 199–232. 45 indexed citations

18.

Chudyk, Ewa I., Matteo Aldeghi, Dmitri G. Fedorov, et al.. (2017). Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. Methods in molecular biology. 1705. 179–195. 14 indexed citations

19.

Biggin, Philip C., Matteo Aldeghi, Michael J. Bodkin, & Alexander Heifetz. (2016). Beyond Membrane Protein Structure: Drug Discovery, Dynamics and Difficulties. Advances in experimental medicine and biology. 922. 161–181. 4 indexed citations

20.

Aldeghi, Matteo, Alexander Heifetz, Michael J. Bodkin, Stefan Knapp, & Philip C. Biggin. (2015). Accurate calculation of the absolute free energy of binding for drug molecules. Chemical Science. 7(1). 207–218. 255 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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