Switch to English
VŠCHT PrahaFCHTÚstav informatiky a chemie Výzkum na ústavu Chemoinformatika Publikace

Publikace skupiny Chemoinformatika

 

  1. McKenna, S. M.; Šícho, M.; van der Horst, C.; Maasland, J.; van der Nol, E.; Turco, G.; Bernatavicius, A.; van Westen, G. J.; Heitman, L. H.; Pomplun, S. J. Automated Parallel Synthesis Accelerates Virtual Screening Hit Discovery. Journal of the American Chemical Society 2026. DOI: 10.1021/jacs.6c05055.
  2. Fil, V.; van den Broek, R. L.; Šícho, M.; Čmelo, I.; Lorente, M. I. A.; Jespers, W.; Van Westen, G. J. P.; Svozil, D. Scaffold-based evaluation metrics for fair comparison of molecular generators. Journal of Cheminformatics 2026, 18 (1), 8-13. DOI: 10.1186/s13321-026-01213-6.
  3. Essa, K.; van der Dussen, K. N.; Yao, Y.; Bleijs, B.; Zacarias, N. V. O.; Heitman, L. H.; van Westen, G. J.; Jespers, W.; van der Es, D.; Šícho, M. Combining AlphaFold with Focused Virtual Library Design in the Development of Novel CCR2 and CCR5 Antagonists. Journal of Chemical Information and Modeling 2025. DOI: 10.1021/acs.jcim.5c01596.
  4. Fülöp, J.; Šícho, M.; Dehaen, W. TEMPL: A Template-Based Protein–Ligand Pose Prediction Baseline. Journal of Chemical Information and Modeling 2025. DOI: 10.1021/acs.jcim.5c01985.
  5. Hassen, A. K.; Šícho, M.; van Aalst, Y. J.; Huizenga, M. C. W.; Reynolds, D. N. R.; Luukkonen, S.; Bernatavicius, A.; Clevert, D.-A.; Janssen, A. P. A.; van Westen, G. J. P.; Preuss, M. Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design. Journal of Cheminformatics 2025. DOI: 10.1186/s13321-024-00910-4.
  6. van den Maagdenberg, H. W.; Šícho, M.; Araripe, D. A.; Luukkonen, S.; Schoenmaker, L.; Jespers, M.; Béquignon, O. J. M.; González, M. G.; van den Broek, R. L.; Bernatavicius, A.; van Hasselt, J. G. C.; van der Graaf, P. H.; van Westen, G. J. P. QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool. Journal of Cheminformatics 2024. DOI: 10.1186/s13321-024-00908-y.
  7. Hunklinger, A.; Hartog, P.; Šícho, M.; Godin, G.; Igor, V Tetko. The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge. SLAS Discovery 2024. DOI: 10.1016/j.slasd.2024.01.005.
  8. Agea, M.; Čmelo, I.; Dehaen, W.; Chen, Y.; Kirchmair, J.; Sedlák, D.; Bartůněk, P.; Šícho, M.; Svozil, D. Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library. Molecular Informatics 2024. DOI: 10.1002/minf.202300316.
  9. Bernatavicius, A.; Šícho, M.; Janssen, A.; Hassen, A.; Preuss, M.; van Westen, G. AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative Models. Journal of Chemical Information and Modeling 2024, 64 (21), 8113-8122. DOI: 10.1021/acs.jcim.4c00309.
  10. Šícho, M.; Luukkonen, S.; van den Maagdenberg, H.; Schoenmaker, L.; Béquignon, O.; van Westen, G. DrugEx: Deep Learning Models and Tools for Exploration of Drug-Like Chemical Space. Journal of Chemical Information and Modeling 2023, 63 (12), 3629-3636. DOI: 10.1021/acs.jcim.3c00434.
  11. Šícho, M.; Liu, X.; Svozil, D.; van Westen, G. J. P. GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics. Journal of Cheminformatics 2021. DOI: 10.1186/s13321-021-00550-y.
  12. Plonka, W.; Stork, C.; Šícho, M.; Kirchmair, J. CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes. BIOORGANIC & MEDICINAL CHEMISTRY 2021. DOI: 10.1016/j.bmc.2021.116388.
  13. NERDD: a web portal providing access to in silico tools for drug discovery. Bioinformatics (Oxford, England) 2020. DOI: 10.1093/bioinformatics/btz695.
  14. Kops, C. D. B.; Šícho, M.; Mazzolari, A.; Kirchmair, J. GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics. Chemical Research in Toxicology 2020, 34 (2), 286-299. DOI: 10.1021/acs.chemrestox.0c00224.
  15. GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism. Frontiers in chemistry 2019. DOI: 10.3389/fchem.2019.00402.
  16. Stork, C.; Chen, Y.; Šícho, M.; Kirchmair, J. Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters. Journal of Chemical Information and Modeling 2019, 59 (3), 1030-1043. DOI: 10.1021/acs.jcim.8b00677.
  17. Šícho, M.; Stork, C.; Mazzolari, A.; De Bruyn Kops, C.; Pedretti, A.; Testa, B.; Vistoli, G.; Svozil, D.; Kirchmair, J. FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes. Journal of Chemical Information and Modeling 2019, 59 (8), 3400-3412. DOI: 10.1021/acs.jcim.9b00376.
  18. Comment on "The power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability". Journal of cheminformatics 2018. DOI: 10.1186/s13321-018-0267-x.
  19. Stork, C.; Wagner, J.; Friedrich, N.-O.; de Bruyn Kops, C.; Šícho, M.; Kirchmair, J. Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters. ChemMedChem 2018, 13 (6), 564-571. DOI: 10.1002/cmdc.201700673.
  20. Molecular docking as a tool to virtually develop drugs. Chemicke Listy 2017, 111, 754-759.
  21. Šícho, M.; Svozil, D. MOLEKULOVÉ DOKOVÁNÍ JAKO NÁSTROJ PRO VIRTUÁLNÍ NÁVRH LÉČIV. Chemické listy 2017, 111 (11), 754-759.
  22. Šícho, M.; Kops, C. D. B.; Stork, C.; Svozil, D.; Kirchmair, J. FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity. Journal of Chemical Information and Modeling 2017, 57 (8), 1832-1846. DOI: 10.1021/acs.jcim.7b00250.
  23. Šícho, M.; Svozil, D.; Hoksza, D. Activity-driven exploration of chemical space with morphing. In Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015; 2015; pp 1024-1031. DOI: 10.1109/BIBM.2015.7359824.