Noé Sturm

849 total citations
16 papers, 418 citations indexed

About

Noé Sturm is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Noé Sturm has authored 16 papers receiving a total of 418 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Theory and Mathematics, 8 papers in Molecular Biology and 7 papers in Materials Chemistry. Recurrent topics in Noé Sturm's work include Computational Drug Discovery Methods (14 papers), Machine Learning in Materials Science (6 papers) and Microbial Natural Products and Biosynthesis (5 papers). Noé Sturm is often cited by papers focused on Computational Drug Discovery Methods (14 papers), Machine Learning in Materials Science (6 papers) and Microbial Natural Products and Biosynthesis (5 papers). Noé Sturm collaborates with scholars based in Sweden, Australia and Germany. Noé Sturm's co-authors include Ola Engkvist, Hongming Chen, Giulio Rastelli, Annachiara Tinivella, E. Vila, Luca Pinzi, Esther Kellenberger, Ronald J. Quinn, Didier Rognan and Jérémy Desaphy and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Medicinal Chemistry and Molecules.

In The Last Decade

Noé Sturm

16 papers receiving 405 citations

Peers

Noé Sturm

CTM

PHARM

Shuaishi Gao China

Emma J. Manners United Kingdom

Fangjin Chen China

Nicolas Bosc United Kingdom

Sybilla Corbett United Kingdom

Tevfik Kizilören United Kingdom

Harris Ioannidis United Kingdom

Saw Simeon Thailand

Tiago Alves de Oliveira Brazil

Andreas Verras United States

Shuaishi Gao China

Noé Sturm

296 ×1.0

CTM

334 ×1.4

45 ×0.7

PHARM

74 ×1.1

46 ×0.9

Citations per year, relative to Noé Sturm Noé Sturm (= 1×) peers Shuaishi Gao

Countries citing papers authored by Noé Sturm

Since Specialization

Citations

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

Fields of papers citing papers by Noé Sturm

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Noé Sturm

This figure shows the co-authorship network connecting the top 25 collaborators of Noé Sturm. A scholar is included among the top collaborators of Noé Sturm 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 Noé Sturm. Noé Sturm is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Sturm, Noé, Peter S. Kutchukian, William C. Forrester, et al.. (2024). Machine learning proteochemometric models for Cereblon glue activity predictions. SHILAP Revista de lepidopterología. 6. 100100–100100. 4 indexed citations

Beckers, Maximilian, Noé Sturm, Finton Sirockin, Nikolas Fechner, & Nikolaus Stiefl. (2023). Prediction of Small-Molecule Developability Using Large-Scale In Silico ADMET Models. Journal of Medicinal Chemistry. 66(20). 14047–14060. 11 indexed citations

Heyndrickx, Wouter, Ádám Arany, Jaak Simm, et al.. (2023). Conformal efficiency as a metric for comparative model assessment befitting federated learning. SHILAP Revista de lepidopterología. 3. 100070–100070. 3 indexed citations

Simm, Jaak, Lina Humbeck, Adam Zalewski, et al.. (2021). Splitting chemical structure data sets for federated privacy-preserving machine learning. Journal of Cheminformatics. 13(1). 96–96. 31 indexed citations

Trapotsi, Maria‐Anna, Lewis Mervin, Avid M. Afzal, et al.. (2021). Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions. Journal of Chemical Information and Modeling. 61(3). 1444–1456. 25 indexed citations

Humbeck, Lina, Tobias Morawietz, Noé Sturm, et al.. (2021). Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models. Molecules. 26(22). 6959–6959. 8 indexed citations

Sturm, Noé, Andreas Mayr, Vladimir Chupakhin, et al.. (2020). Industry-scale application and evaluation of deep learning for drug target prediction. Journal of Cheminformatics. 12(1). 26–26. 29 indexed citations

Sturm, Noé, et al.. (2019). Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability. Journal of Cheminformatics. 11(1). 54–54. 28 indexed citations

Walsh, Jarrod, Noé Sturm, Isabella Feierberg, et al.. (2019). Identification of Compounds That Interfere with High‐Throughput Screening Assay Technologies. ChemMedChem. 14(20). 1795–1802. 24 indexed citations

10.

Sturm, Noé, Jiangming Sun, Andreas Mayr, et al.. (2018). Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models. Journal of Chemical Information and Modeling. 59(3). 962–972. 28 indexed citations

11.

Sturm, Noé, Annachiara Tinivella, & Giulio Rastelli. (2018). Exploration and Comparison of the Geometrical and Physicochemical Properties of an αC Allosteric Pocket in the Structural Kinome. Journal of Chemical Information and Modeling. 58(5). 1094–1103. 7 indexed citations

12.

Vila, E., Luca Pinzi, Noé Sturm, et al.. (2017). On the Integration of In Silico Drug Design Methods for Drug Repurposing. Frontiers in Pharmacology. 8. 298–298. 155 indexed citations

13.

Sturm, Noé, Ronald J. Quinn, & Esther Kellenberger. (2017). Structural Searching of Biosynthetic Enzymes to Predict Protein Targets of Natural Products. Planta Medica. 84(5). 304–310. 6 indexed citations

14.

Sturm, Noé, Didier Rognan, Ronald J. Quinn, & Esther Kellenberger. (2016). Comparing Atom-Based with Residue-Based Descriptors in Predicting Binding Site Similarity: Do Backbone Atoms Matter?. Future Medicinal Chemistry. 8(15). 1871–1885. 3 indexed citations

15.

Sturm, Noé, Ronald J. Quinn, & Esther Kellenberger. (2015). Similarity between Flavonoid Biosynthetic Enzymes and Flavonoid Protein Targets Captured by Three-Dimensional Computing Approach. Planta Medica. 81(6). 467–473. 9 indexed citations

16.

Sturm, Noé, Jérémy Desaphy, Ronald J. Quinn, Didier Rognan, & Esther Kellenberger. (2012). Structural Insights into the Molecular Basis of the Ligand Promiscuity. Journal of Chemical Information and Modeling. 52(9). 2410–2421. 47 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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