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Martin Šícho

Showing results (1-10 of 17) with videos related to

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Journal of Chemical Information and Modeling|October 14, 2025
TEMPL: A Template-Based Protein-Ligand Pose Prediction BaselineJozef Fülöp, Martin Šícho, Wim Dehaen
Bioorganic & Medicinal Chemistry|September 6, 2021
CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymesWojciech Plonka, Conrad Stork, Martin Šícho, et al.
Journal of Chemical Information and Modeling|January 10, 2019
Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent HittersConrad Stork, Ya Chen, Martin Šícho, et al.
Chemical Research in Toxicology|August 14, 2020
GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of XenobioticsChristina de Bruyn Kops, Martin Šícho, Angelica Mazzolari, et al.
Journal of Chemical Information and Modeling|August 8, 2017
FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 RegioselectivityMartin Šícho, Christina de Bruyn Kops, Conrad Stork, et al.
SLAS Discovery : Advancing Life Sciences R & D|February 5, 2024
The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challengeAndrea Hunklinger, Peter Hartog, Martin Šícho, et al.
Chemmedchem|December 30, 2017
Hit Dexter: A Machine-Learning Model for the Prediction of Frequent HittersConrad Stork, Johannes Wagner, Nils-Ole Friedrich, et al.
Frontiers in Chemistry|June 29, 2019
GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of MetabolismChristina de Bruyn Kops, Conrad Stork, Martin Šícho, et al.
Bioinformatics (Oxford, England)|February 21, 2020
NERDD: a web portal providing access to in silico tools for drug discoveryConrad Stork, Gerd Embruch, Martin Šícho, et al.
Journal of Chemical Information and Modeling|October 30, 2024
AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative ModelsAndrius Bernatavicius, Martin Šícho, Antonius P A Janssen, et al.
Pageof 2

Showing results (1-10 of 17) with videos related to

Sort By:
Pageof 2
Journal of Chemical Information and Modeling|October 14, 2025
TEMPL: A Template-Based Protein-Ligand Pose Prediction BaselineJozef Fülöp, Martin Šícho, Wim Dehaen
Bioorganic & Medicinal Chemistry|September 6, 2021
CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymesWojciech Plonka, Conrad Stork, Martin Šícho, et al.
Journal of Chemical Information and Modeling|January 10, 2019
Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent HittersConrad Stork, Ya Chen, Martin Šícho, et al.
Chemical Research in Toxicology|August 14, 2020
GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of XenobioticsChristina de Bruyn Kops, Martin Šícho, Angelica Mazzolari, et al.
Journal of Chemical Information and Modeling|August 8, 2017
FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 RegioselectivityMartin Šícho, Christina de Bruyn Kops, Conrad Stork, et al.
SLAS Discovery : Advancing Life Sciences R & D|February 5, 2024
The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challengeAndrea Hunklinger, Peter Hartog, Martin Šícho, et al.
Chemmedchem|December 30, 2017
Hit Dexter: A Machine-Learning Model for the Prediction of Frequent HittersConrad Stork, Johannes Wagner, Nils-Ole Friedrich, et al.
Frontiers in Chemistry|June 29, 2019
GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of MetabolismChristina de Bruyn Kops, Conrad Stork, Martin Šícho, et al.
Bioinformatics (Oxford, England)|February 21, 2020
NERDD: a web portal providing access to in silico tools for drug discoveryConrad Stork, Gerd Embruch, Martin Šícho, et al.
Journal of Chemical Information and Modeling|October 30, 2024
AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative ModelsAndrius Bernatavicius, Martin Šícho, Antonius P A Janssen, et al.
Pageof 2