Teodoro Laino

10.7k total citations · 1 hit paper
95 papers, 4.2k citations indexed

About

Teodoro Laino is a scholar working on Materials Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Teodoro Laino has authored 95 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Materials Chemistry, 29 papers in Molecular Biology and 19 papers in Computational Theory and Mathematics. Recurrent topics in Teodoro Laino's work include Machine Learning in Materials Science (30 papers), Computational Drug Discovery Methods (19 papers) and Topic Modeling (8 papers). Teodoro Laino is often cited by papers focused on Machine Learning in Materials Science (30 papers), Computational Drug Discovery Methods (19 papers) and Topic Modeling (8 papers). Teodoro Laino collaborates with scholars based in Switzerland, Italy and United States. Teodoro Laino's co-authors include Philippe Schwaller, Alessandro Curioni, Alain C. Vaucher, Jean‐Louis Reymond, Michele Parrinello, Vishnu H Nair, Alessandro Laio, Fawzi Mohamed, Costas Bekas and Katharina Meier and has published in prestigious journals such as Journal of the American Chemical Society, Physical Review Letters and Nature Communications.

In The Last Decade

Teodoro Laino

92 papers receiving 4.1k citations

Hit Papers

Accelerating materials di... 2022 2026 2023 2024 2022 50 100 150 200
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.

  1. Accelerating materials discovery using artificial intelligence, high performance computing and robotics
    2022 241 citations Edward O. Pyzer‐Knapp, Peter Staar et al. npj Computational Materials profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Teodoro Laino 2.1k 1.2k 1.1k 796 722 95 4.2k
Rafael Gómez‐Bombarelli 2.4k 1.2× 1.1k 0.9× 1.3k 1.2× 1.4k 1.7× 419 0.6× 126 5.1k
Pascal Friederich 2.0k 1.0× 633 0.5× 803 0.7× 1.4k 1.7× 351 0.5× 125 4.0k
Justin S. Smith 3.6k 1.7× 1.5k 1.2× 2.1k 1.8× 423 0.5× 361 0.5× 68 5.0k
Pavlo O. Dral 3.1k 1.5× 993 0.8× 1.8k 1.6× 483 0.6× 267 0.4× 82 4.3k
Heather J. Kulik 4.3k 2.1× 1.2k 1.0× 1.0k 0.9× 1.2k 1.6× 818 1.1× 220 7.5k
Rohit Batra 3.2k 1.5× 468 0.4× 793 0.7× 1.3k 1.6× 504 0.7× 54 4.6k
Maxim V. Fedorov 1.2k 0.6× 892 0.7× 521 0.5× 1.2k 1.4× 740 1.0× 127 5.3k
Philippe Schwaller 2.9k 1.4× 852 0.7× 1.3k 1.2× 539 0.7× 481 0.7× 51 3.9k
Benjamín Sánchez-Lengeling 2.7k 1.3× 1.0k 0.9× 1.7k 1.5× 1.7k 2.2× 465 0.6× 28 5.3k
Mark P. Waller 2.0k 1.0× 1.7k 1.4× 1.7k 1.5× 183 0.2× 437 0.6× 52 4.2k

Countries citing papers authored by Teodoro Laino

Since Specialization
Citations

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

Fields of papers citing papers by Teodoro Laino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Teodoro Laino

This figure shows the co-authorship network connecting the top 25 collaborators of Teodoro Laino. A scholar is included among the top collaborators of Teodoro Laino 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 Teodoro Laino. Teodoro Laino 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.
Hartrampf, Nina, et al.. (2025). From Spectra to Structure: AI-Powered 31 P NMR Interpretation. Analytical Chemistry. 97(29). 15736–15742.
2.
Zipoli, Federico, et al.. (2025). IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic molecules. Scientific Data. 12(1). 1375–1375. 1 indexed citations
3.
Vaucher, Alain C., et al.. (2025). Negative chemical data boosts language models in reaction outcome prediction. Science Advances. 11(24). eadt5578–eadt5578. 1 indexed citations
4.
Pyzer‐Knapp, Edward O., Matteo Manica, Peter Staar, et al.. (2025). Foundation models for materials discovery – current state and future directions. npj Computational Materials. 11(1). 20 indexed citations
5.
Zipoli, Federico, et al.. (2024). Integrating genetic algorithms and language models for enhanced enzyme design. Briefings in Bioinformatics. 26(1). 2 indexed citations
6.
Zipoli, Federico, Carlo Baldassari, Matteo Manica, Jannis Born, & Teodoro Laino. (2024). Growing strings in a chemical reaction space for searching retrosynthesis pathways. npj Computational Materials. 10(1). 5 indexed citations
7.
Manica, Matteo, et al.. (2024). Predicting polymerization reactions via transfer learning using chemical language models. npj Computational Materials. 10(1). 9 indexed citations
8.
Laino, Teodoro, et al.. (2024). Leveraging infrared spectroscopy for automated structure elucidation. Communications Chemistry. 7(1). 268–268. 15 indexed citations
9.
Zipoli, Federico, et al.. (2024). Completion of partial chemical equations. Machine Learning Science and Technology. 5(2). 25071–25071. 3 indexed citations
10.
Schilter, Oliver, et al.. (2023). The Role of AI in Driving the Sustainability of the Chemical Industry. CHIMIA International Journal for Chemistry. 77(3). 144–144. 6 indexed citations
11.
Unsleber, Jan P., Alain C. Vaucher, Thomas Weymuth, et al.. (2023). Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning. Digital Discovery. 2(3). 663–673. 7 indexed citations
12.
Leonov, Artem I., et al.. (2023). Tools for Synthesis Planning, Automation, and Analytical Data Analysis. CHIMIA International Journal for Chemistry. 77(1/2). 17–17. 1 indexed citations
13.
Vaucher, Alain C., et al.. (2023). Enhancing diversity in language based models for single-step retrosynthesis. Digital Discovery. 2(2). 489–501. 11 indexed citations
14.
Schilter, Oliver, Alain C. Vaucher, Philippe Schwaller, & Teodoro Laino. (2023). Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions. Digital Discovery. 2(3). 728–735. 26 indexed citations
15.
Castrogiovanni, Alessandro, Théophile Gaudin, Teodoro Laino, et al.. (2023). Fuelling the Digital Chemistry Revolution with Language Models. CHIMIA International Journal for Chemistry. 77(7/8). 484–484. 1 indexed citations
16.
Schwaller, Philippe, Alain C. Vaucher, Rubén Laplaza, et al.. (2022). Machine intelligence for chemical reaction space. Wiley Interdisciplinary Reviews Computational Molecular Science. 12(5). 61 indexed citations
17.
Schwaller, Philippe, Alain C. Vaucher, Teodoro Laino, & Jean‐Louis Reymond. (2021). Prediction of chemical reaction yields using deep learning. Machine Learning Science and Technology. 2(1). 15016–15016. 170 indexed citations
18.
Gaudin, Théophile, Oliver Schilter, Federico Zipoli, & Teodoro Laino. (2020). Advanced Data-Driven Manufacturing.. ERCIM news/ERCIM news online edition. 2020. 1 indexed citations
19.
Chenthamarakshan, Vijil, Payel Das, Samuel C. Hoffman, et al.. (2020). CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models. neural information processing systems. 33. 4320–4332. 30 indexed citations
20.
Bon, Marta, et al.. (2018). Revealing the role of phosphoric acid in all-vanadium redox flow batteries with DFT calculations and in situ analysis. Physical Chemistry Chemical Physics. 20(36). 23664–23673. 22 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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