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Journal of Cheminformatics
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October 3, 2021
Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization
Jules Leguy, Marta Glavatskikh, Thomas Cauchy, et al.
Journal of Cheminformatics
|
January 12, 2021
Dataset's chemical diversity limits the generalizability of machine learning predictions
Marta Glavatskikh, Jules Leguy, Gilles Hunault, et al.
Journal of Cheminformatics
|
January 12, 2021
EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation
Jules Leguy, Thomas Cauchy, Marta Glavatskikh, et al.
Molecular Informatics
|
August 17, 2016
Predictive Models for the Free Energy of Hydrogen Bonded Complexes with Single and Cooperative Hydrogen Bonds
Marta Glavatskikh, Timur Madzhidov, Vitaly Solov'ev, et al.
ACS Chemical Biology
|
December 9, 2022
Enzymatic Macrolactamization of mRNA Display Libraries for Inhibitor Selection
Matthew M Bowler, Marta Glavatskikh, Chad V Pecot, et al.
Molecular Informatics
|
July 25, 2018
Visualization and Analysis of Complex Reaction Data: The Case of Tautomeric Equilibria
Marta Glavatskikh, Timur Madzhidov, Igor I Baskin, et al.
Molecular Informatics
|
August 23, 2018
Predictive Models for Kinetic Parameters of Cycloaddition Reactions
Marta Glavatskikh, Timur Madzhidov, Dragos Horvath, et al.
Molecular Informatics
|
August 6, 2016
Predictive Models for Halogen-bond Basicity of Binding Sites of Polyfunctional Molecules
Marta Glavatskikh, Timur Madzhidov, Vitaly Solov'ev, et al.
European Journal of Medicinal Chemistry
|
December 10, 2022
Combining pharmacophore models derived from DNA-encoded chemical libraries with structure-based exploration to predict Tankyrase 1 inhibitors
Alba L Montoya, Marta Glavatskikh, Brayden J Halverson, et al.
Nature Communications
|
July 2, 2024
In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot
Yi An, Jiwoong Lim, Marta Glavatskikh, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 13) with videos related to
Sort By:
Page
of 2
Journal of Cheminformatics
|
October 3, 2021
Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization
Jules Leguy, Marta Glavatskikh, Thomas Cauchy, et al.
Journal of Cheminformatics
|
January 12, 2021
Dataset's chemical diversity limits the generalizability of machine learning predictions
Marta Glavatskikh, Jules Leguy, Gilles Hunault, et al.
Journal of Cheminformatics
|
January 12, 2021
EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation
Jules Leguy, Thomas Cauchy, Marta Glavatskikh, et al.
Molecular Informatics
|
August 17, 2016
Predictive Models for the Free Energy of Hydrogen Bonded Complexes with Single and Cooperative Hydrogen Bonds
Marta Glavatskikh, Timur Madzhidov, Vitaly Solov'ev, et al.
ACS Chemical Biology
|
December 9, 2022
Enzymatic Macrolactamization of mRNA Display Libraries for Inhibitor Selection
Matthew M Bowler, Marta Glavatskikh, Chad V Pecot, et al.
Molecular Informatics
|
July 25, 2018
Visualization and Analysis of Complex Reaction Data: The Case of Tautomeric Equilibria
Marta Glavatskikh, Timur Madzhidov, Igor I Baskin, et al.
Molecular Informatics
|
August 23, 2018
Predictive Models for Kinetic Parameters of Cycloaddition Reactions
Marta Glavatskikh, Timur Madzhidov, Dragos Horvath, et al.
Molecular Informatics
|
August 6, 2016
Predictive Models for Halogen-bond Basicity of Binding Sites of Polyfunctional Molecules
Marta Glavatskikh, Timur Madzhidov, Vitaly Solov'ev, et al.
European Journal of Medicinal Chemistry
|
December 10, 2022
Combining pharmacophore models derived from DNA-encoded chemical libraries with structure-based exploration to predict Tankyrase 1 inhibitors
Alba L Montoya, Marta Glavatskikh, Brayden J Halverson, et al.
Nature Communications
|
July 2, 2024
In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot
Yi An, Jiwoong Lim, Marta Glavatskikh, et al.
Page
of 2