PharmAI's technology is based on over 100 man years of academic research. Our scientists continuously improve our algorithm to better and faster optimize our drug discovery engine. Below is a selected list of publications that is the base of the PharmAIs algorithm.
AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1, Anastasiia Gryniukova, Florian Kaiser, Iryna Myziuk, Diana Alieksieieva, Christoph Leberecht, Peter P. Heym, Olga O. Tarkhanova, Yurii S. Moroz, Petro BoryskoV, and V. Joachim Haupt (2023) https://doi.org/10.1021/acs.jmedchem.3c00128
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An interaction-based drug discovery screen explains known SARS-CoV-2 inhibitors and predicts new compound scaffolds, Philipp Schake, Klevia Dishnica, Florian Kaiser, Christoph Leberecht, V. Joachim Haupt & Michael Schroeder (2023) https://doi.org/10.1038/s41598-023-35671-x
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PLIP 2021: expanding the scope of the protein–ligand interaction profiler to DNA and RNA Melissa F Adasme, Katja L Linnemann, Sarah Naomi Bolz, Florian Kaiser, Sebastian Salentin, V Joachim Haupt, Michael Schroeder Nucleic Acids Research, gkab294, https://doi.org/10.1093/nar/gkab294
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In Silico Driven Prediction of MAPK14 Off-Targets Reveals Unrelated Proteins with High Accuracy Florian Kaiser, Maximilian G. Plach, Christoph Leberecht, Thomas Schubert, V. Joachim Haupt bioRxiv 2020.07.24.219071; doi: https://doi.org/10.1101/2020.07.24.219071.
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Structure-based drug repositioning explains ibrutinib as VEGFR2 inhibitor, Melissa F. Adasme, Daniele Parisi, Kristien Van Belle,Sebastian Salentin,V. Joachim Haupt,Gary S. Jennings,Jörg-Christian Heinrich,Jean Herman,Ben Sprangers,Thierry Louat,Yves Moreau,Michael Schroeder, (2020), https://doi.org/10.1371/journal.pone.0233089
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The structural basis of the genetic code: amino acid recognition by aminoacyl-tRNA synthetases. Kaiser et al (2020)
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Application of our Focused Library Service. Kaiser F, et al. (2020) Focus Your Screening Library: Rapid Identification of Novel PDE2 Inhibitors with in silico Driven Library Prioritization and MicroScale Thermophoresis. bioRxiv 2020.04.22.021360; doi: https://doi.org/10.1101/2020.04.22.021360
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Drug Repositioning from Infectious Disease (Malaria) to Cancer Chemoresistance. Salentin S, et al. (2017) From malaria to cancer: Computational drug repositioning of amodiaquine using PLIP interaction patterns. Sci Rep. 7:11401
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New Drug Candidates for Cancer Chemoresistance. Heinrich JC, et al. (2016) New HSP27 inhibitors efficiently suppress drug resistance development in cancer cells. Oncotarget. 7:68156
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New Drug Candidates for Tuberculosis. Štular T, et al. (2016) Discovery of M. tuberculosis InhA inhibitors by binding sites comparison and ligands prediction. J Med Chem. 59:11069-11078
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Drug Repositioning from Antiviral to Chagas Disease. Haupt VJ, et al. (2016) Computational Drug Repositioning by Target Hopping: A Use Case in Chagas Disease. Curr Pharm Des. 22:3124
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Interaction Patterns for Characterization of Ligand Binding. Salentin S, et al. (2015) PLIP: fully automated protein-ligand interaction profiler. Nucl Acids Res. 43:443
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Binding Site Similarity Largely Explains Drug Promiscuity. Haupt VJ, et al. (2013) Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key. PLOS ONE 8:10.1371 (Top 10% most cited in PLOS ONE)
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Binding Site Similarity Largely Explains Drug Promiscuity. Haupt VJ, et al. (2013) Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key. PLOS ONE 8:10.1371 (Top 10% most cited in PLOS ONE)
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