Gisbert Schneider

31.9k total citations · 11 hit papers
453 papers, 22.9k citations indexed

About

Gisbert Schneider is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Gisbert Schneider has authored 453 papers receiving a total of 22.9k indexed citations (citations by other indexed papers that have themselves been cited), including 324 papers in Molecular Biology, 240 papers in Computational Theory and Mathematics and 75 papers in Pharmacology. Recurrent topics in Gisbert Schneider's work include Computational Drug Discovery Methods (238 papers), Chemical Synthesis and Analysis (99 papers) and Microbial Natural Products and Biosynthesis (51 papers). Gisbert Schneider is often cited by papers focused on Computational Drug Discovery Methods (238 papers), Chemical Synthesis and Analysis (99 papers) and Microbial Natural Products and Biosynthesis (51 papers). Gisbert Schneider collaborates with scholars based in Switzerland, Germany and Austria. Gisbert Schneider's co-authors include Petra Schneider, Jan A. Hiss, Uli Fechner, Daniel Reker, Tiago Rodrigues, Christopher D. Fjell, Robert E. W. Hancock, Francesca Grisoni, Paul Wrede and Evgeny Byvatov 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

Gisbert Schneider

446 papers receiving 22.2k citations

Hit Papers

Designing antimicrobial peptides: form follows f... 1999 2026 2008 2017 2011 2016 2005 2019 1999 500 1000 1.5k
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. Designing antimicrobial peptides: form follows function
    2011 1.6k citations Jan A. Hiss, Gisbert Schneider et al. Nature Reviews Drug Discovery profile →
  2. Counting on natural products for drug design
    2016 930 citations Petra Schneider, Gisbert Schneider et al. Nature Chemistry profile →
  3. Computer-based de novo design of drug-like molecules
    2005 630 citations Gisbert Schneider, Uli Fechner Nature Reviews Drug Discovery profile →
  4. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
    2019 628 citations Ryan Byrne, Gisbert Schneider et al. profile →
  5. “Scaffold-Hopping” by Topological Pharmacophore Search: A Contribution to Virtual Screening
    1999 543 citations Gisbert Schneider et al. Angewandte Chemie International Edition profile →
  6. Automating drug discovery
    2017 525 citations Gisbert Schneider Nature Reviews Drug Discovery profile →
  7. Rethinking drug design in the artificial intelligence era
    2019 496 citations Petra Schneider, Gisbert Schneider et al. Nature Reviews Drug Discovery profile →
  8. Deep Learning in Drug Discovery
    2015 460 citations Jan A. Hiss, Gisbert Schneider et al. profile →
  9. Predicting drug metabolism: experiment and/or computation?
    2015 359 citations Gisbert Schneider et al. Nature Reviews Drug Discovery profile →
  10. Artificial intelligence in drug discovery: recent advances and future perspectives
    2021 243 citations Francesca Grisoni, Gisbert Schneider et al. profile →
  11. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
    2023 145 citations Alexander Tropsha, Olexandr Isayev et al. Nature Reviews Drug Discovery profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gisbert Schneider Switzerland 70 13.3k 10.2k 3.7k 3.3k 2.9k 453 22.9k
Jean‐Louis Reymond Switzerland 67 10.8k 0.8× 5.1k 0.5× 4.0k 1.1× 4.1k 1.2× 1.0k 0.3× 406 17.3k
Roman A. Laskowski United Kingdom 51 33.9k 2.6× 4.6k 0.5× 8.4k 2.2× 3.4k 1.0× 2.2k 0.8× 109 47.1k
Tingjun Hou China 81 16.1k 1.2× 10.6k 1.0× 5.4k 1.4× 3.8k 1.1× 1.9k 0.7× 554 29.3k
Garrett M. Morris United Kingdom 38 21.1k 1.6× 9.7k 1.0× 3.1k 0.8× 9.3k 2.8× 3.8k 1.3× 123 38.5k
David S. Goodsell United States 55 27.7k 2.1× 10.6k 1.0× 4.1k 1.1× 10.7k 3.2× 4.1k 1.4× 290 47.0k
Oleg Trott United States 7 14.2k 1.1× 6.5k 0.6× 1.8k 0.5× 6.2k 1.8× 2.9k 1.0× 7 27.3k
Torsten Schwede Switzerland 53 25.7k 1.9× 2.7k 0.3× 4.0k 1.1× 1.6k 0.5× 1.7k 0.6× 108 38.8k
Michel F. Sanner United States 28 14.0k 1.1× 6.1k 0.6× 2.1k 0.5× 6.0k 1.8× 2.5k 0.8× 53 25.6k
Ruth Nussinov United States 112 44.1k 3.3× 9.9k 1.0× 10.6k 2.8× 2.5k 0.8× 1.7k 0.6× 795 54.6k
Tom L. Blundell United Kingdom 94 34.2k 2.6× 5.3k 0.5× 7.7k 2.0× 3.3k 1.0× 1.7k 0.6× 505 45.7k

Countries citing papers authored by Gisbert Schneider

Since Specialization
Citations

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

Fields of papers citing papers by Gisbert Schneider

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gisbert Schneider

This figure shows the co-authorship network connecting the top 25 collaborators of Gisbert Schneider. A scholar is included among the top collaborators of Gisbert Schneider 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 Gisbert Schneider. Gisbert Schneider 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.
2.
Albrecht, Manuela, et al.. (2025). Discovery of 1,3,4‐Oxadiazin‐5‐One Derivative CJ1‐34 as a Partial ATP Synthase Inhibitor for CNS Applications. Chemistry - A European Journal. 31(24). e202404517–e202404517. 1 indexed citations
3.
Isert, Clemens, Kenneth Atz, Sereina Riniker, & Gisbert Schneider. (2024). Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning. RSC Advances. 14(7). 4492–4502. 11 indexed citations
4.
Grisoni, Francesca, Petra Schneider, Georg Aichinger, et al.. (2024). Baricitinib and tofacitinib off‐target profile, with a focus on Alzheimer's disease. Alzheimer s & Dementia Translational Research & Clinical Interventions. 10(1). e12445–e12445. 6 indexed citations
5.
Atz, Kenneth, Leandro Cotos, Clemens Isert, et al.. (2024). Prospective de novo drug design with deep interactome learning. Nature Communications. 15(1). 3408–3408. 44 indexed citations
6.
Atz, Kenneth, Alex T. Müller, Andrea Anelli, et al.. (2024). Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry. RSC Medicinal Chemistry. 15(7). 2310–2321. 4 indexed citations
7.
Isert, Clemens, et al.. (2023). QMugs 1.1: Quantum mechanical properties of organic compounds commonly encountered in reactivity datasets. Chemical Data Collections. 46. 101040–101040. 11 indexed citations
8.
Schneider, Gisbert, et al.. (2023). Designing molecules with autoencoder networks. Nature Computational Science. 3(11). 922–933. 11 indexed citations
9.
Atz, Kenneth, Alex T. Müller, Georg Wuitschik, et al.. (2023). Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning. Nature Chemistry. 16(2). 239–248. 50 indexed citations
10.
Tropsha, Alexander, Olexandr Isayev, Alexandre Varnek, Gisbert Schneider, & Artem Cherkasov. (2023). Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nature Reviews Drug Discovery. 23(2). 141–155. 145 indexed citations breakdown →
11.
Atz, Kenneth, Alex T. Müller, Clemens Isert, et al.. (2023). Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening. Communications Chemistry. 6(1). 256–256. 8 indexed citations
12.
Lenhart, Dominik, Ryan Byrne, Wolfgang Schliebs, et al.. (2021). Computer-Aided Design and Synthesis of a New Class of PEX14 Inhibitors: Substituted 2,3,4,5-Tetrahydrobenzo[F][1,4]oxazepines as Potential New Trypanocidal Agents. Journal of Chemical Information and Modeling. 61(10). 5256–5268. 3 indexed citations
13.
Fuchs, Jens A., et al.. (2019). Identification of Chemokine Ligands by Biochemical Fragmentation and Simulated Peptide Evolution. Angewandte Chemie International Edition. 58(21). 7138–7142. 3 indexed citations
14.
Persch, Elke, S. Bryson, Christian Eberle, et al.. (2014). Binding to Large Enzyme Pockets: Small‐Molecule Inhibitors of Trypanothione Reductase. ChemMedChem. 9(8). 1880–1891. 43 indexed citations
15.
Hoy, Benjamin, Martin Löwer, Gert Carra, et al.. (2010). Helicobacter pylori HtrA is a new secreted virulence factor that cleaves E‐cadherin to disrupt intercellular adhesion. EMBO Reports. 11(10). 798–804. 246 indexed citations
16.
Resch, Eduard, Jan A. Hiss, A. Schreiner, Gisbert Schneider, & Anna Starzinski‐Powitz. (2010). Long signal peptides of RGMa and DCBLD2 are dissectible into subdomains according to the NtraC model. Molecular BioSystems. 7(3). 942–951. 4 indexed citations
17.
Tausch, Lars, Ulf Siemoneit, Daniel Poeckel, et al.. (2009). Identification of Human Cathepsin G As a Functional Target of Boswellic Acids from the Anti-Inflammatory Remedy Frankincense. The Journal of Immunology. 183(5). 3433–3442. 70 indexed citations
18.
Perković, Mario, Stanislaw Schmidt, Daniela Marino, et al.. (2008). Species-specific Inhibition of APOBEC3C by the Prototype Foamy Virus Protein Bet. Journal of Biological Chemistry. 284(9). 5819–5826. 61 indexed citations
19.
Fechner, Uli & Gisbert Schneider. (2004). Evaluation of Distance Metrics for Ligand‐Based Similarity Searching. ChemBioChem. 5(4). 538–540. 17 indexed citations
20.
Böhm, Hans‐Joachim & Gisbert Schneider. (2000). Virtual Screening for Bioactive Molecules. John Wiley & Sons, Inc. eBooks. 326–326. 111 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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