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Virginie Y. Martiny

Researcher at Paris Diderot University

Publications -  13
Citations -  374

Virginie Y. Martiny is an academic researcher from Paris Diderot University. The author has contributed to research in topics: Docking (molecular) & Small molecule binding. The author has an hindex of 8, co-authored 12 publications receiving 311 citations. Previous affiliations of Virginie Y. Martiny include French Institute of Health and Medical Research & Institut de Chimie des Substances Naturelles.

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Toward in silico structure-based ADMET prediction in drug discovery.

TL;DR: It is suggested that structure-based ADMET profiling will probably join the mainstream during the coming years following current trends in the field and results suggested that.
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Advances in Molecular Modeling of Human Cytochrome P450 Polymorphism

TL;DR: The importance of understanding the molecular mechanisms related to CYP polymorphism and drug response at the atomic level is outlined and the use of sequence-based and/or protein-structure-based computational approaches to explore these mechanisms is outlined.
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Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6.

TL;DR: An original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling is developed.
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Rational Design of Small-Molecule Stabilizers of Spermine Synthase Dimer by Virtual Screening and Free Energy-Based Approach

TL;DR: It is suggested that druggable pockets exist in the vicinity of the mutation sites at protein-protein interfaces which can be used to alter the disease-causing effects by small molecule binding.
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In Silico Mechanistic Profiling to Probe Small Molecule Binding to Sulfotransferases

TL;DR: This protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase II metabolizing enzymes.