Véronique Stoven

Biography

Véronique Stoven is a researcher specializing in bioinformatics and computational chemistry, with significant expertise in the study of molecular interactions and their application in drug discovery. Her work focuses on developing innovative methods to predict and analyze interactions between therapeutic molecules and their protein targets, particularly in contexts where structural data is limited or absent. She explores integrative approaches combining machine learning, molecular modeling, and systems biology to address challenges such as *scaffold hopping*, the prediction of side effects, and the identification of therapeutic targets in complex diseases such as cystic fibrosis. Her research also includes the optimization of computational pipelines for large-scale virtual screening, as evidenced by her contributions to open-source tools such as *Komet* and *rROMA*, which aim to improve the efficiency and accuracy of predictions in medicinal chemistry and network biology.

Publication(s)

2025
A Molecular Representation to Identify Isofunctional Molecules DOI : 10.1002/minf.202400159
2024
Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset DOI : 10.1021/acs.jcim.4c00422
2024
Representation and quantification of module activity from omics data with rROMA DOI : 10.1038/s41540-024-00331-x
2024
From CFTR to a CF signalling network: a systems biology approach to study Cystic Fibrosis DOI : 10.1186/s12864-024-10752-x
2023
A Network of 17 Microtubule-Related Genes Highlights Functional Deregulations in Breast Cancer DOI : 10.3390/cancers15194870
2023
Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance DOI : 10.1002/minf.202200216
2022
Differential CFTR-Interactome Proximity Labeling Procedures Identify Enrichment in Multiple SLC Transporters DOI : 10.3390/ijms23168937
2022
Profiling the response to lumacaftor-ivacaftor in children with cystic between fibrosis and new insight from a French-Italian real-life cohort DOI : 10.1002/ppul.26123
2021
Drug target identification with machine learning: How to choose negative examples DOI : 10.3390/ijms22105118
2020
Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity DOI : 10.1186/s13321-020-0413-0
2020
Urinary exosomes of patients with cystic fibrosis unravel cftr-related renal disease DOI : 10.3390/ijms21186625
2019
LOTUS: A single- And multitask machine learning algorithm for the prediction of cancer driver genes DOI : 10.1371/journal.pcbi.1007381
2018
Design, synthesis, biological evaluation and cellular imaging of imidazo[4,5-b]pyridine derivatives as potent and selective TAM inhibitors DOI : 10.1016/j.bmc.2018.09.031
2018
Efficient multi-Task chemogenomics for drug specificity prediction DOI : 10.1371/journal.pone.0204999
2017
Kernel Multitask Regression for Toxicogenetics DOI : 10.1002/minf.201700053
2016
Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis DOI : 10.1038/ncomms12460
2016
Multitask feature selection with task descriptors
2015
Prediction of human population responses to toxic compounds by a collaborative competition DOI : 10.1038/nbt.3299
2014
Epigenomic alterations in breast carcinoma from primary tumor to locoregional recurrences DOI : 10.1371/journal.pone.0103986
2013
New aminopyrimidine derivatives as inhibitors of the TAM family DOI : 10.1016/j.ejmech.2013.10.037
2013
In silico screening on the three-dimensional model of the plasmodium vivax sub1 protease leads to the validation of a novel anti-parasite compound DOI : 10.1074/jbc.M113.456764
2013
Inhibitors of the TAM subfamily of tyrosine kinases: Synthesis and biological evaluation DOI : 10.1016/j.ejmech.2012.06.005
2012
A probabilistic model for cell population phenotyping using HCS data DOI : 10.1371/journal.pone.0042715
2012
Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers DOI : 10.1093/bioinformatics/bts412
2012
Relating drug-protein interaction network with drug side effects DOI : 10.1093/bioinformatics/bts383
2011
Extracting sets of chemical substructures and protein domains governing drug-target interactions DOI : 10.1021/ci100476q
2011
Predicting drug side-effect profiles: A chemical fragment-based approach DOI : 10.1186/1471-2105-12-169
2010
A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: Application to ligand prediction DOI : 10.1186/1471-2105-11-99
2009
Insights Into the Enzymatic Mechanism of 6-Phosphogluconolactonase from Trypanosoma brucei Using Structural Data and Molecular Dynamics Simulation DOI : 10.1016/j.jmb.2009.03.063
2008
Virtual screening of the Guanylate Monophosphate Kinase (GMPK) family: Investigating the rules of ligand specificity DOI : 10.2174/157018008784912045
2008
Virtual screening of GPCRs: An in silico chemogenomics approach DOI : 10.1186/1471-2105-9-363
2007
Three Dimensional Structure and Implications for the Catalytic Mechanism of 6-Phosphogluconolactonase from Trypanosoma brucei DOI : 10.1016/j.jmb.2006.11.063
2006
Determination of dihedral Ψ angles in large proteins by combining NHN/CαHα dipole/dipole cross-correlation and chemical shifts DOI : 10.1002/prot.21063
2006
The pharmacophore kernel for virtual screening with support vector machines DOI : 10.1021/ci060138m
2003
Biochemical characterization and NMR studies of the nucleotide-binding domain 1 of multidrug-resistance-associated protein 1: Evidence for interaction between ATP and Trp653 DOI : 10.1042/BJ20030998
2003
Backbone HN, N, Cα, C′, and Cβ assignment of the 6-phosphogluconolactonase, a 266-residue enzyme of the pentose-phosphate pathway from human parasite Trypanosoma brucei [1] DOI : 10.1023/A:1022811124329
2003
An insight into the role of human pancreatic lithostathine
2002
Glutathione levels and BAX activation during apoptosis due to oxidative stress in cells expressing wild-type and mutant cystic fibrosis transmembrane conductance regulator DOI : 10.1074/jbc.M110288200
2001
NMR Spectroscopic Analysis of the First Two Steps of the Pentose-Phosphate Pathway Elucidates the Role of 6-Phosphogluconolactonase DOI : 10.1074/jbc.M105174200
2000
Nucleotide-binding domain 1 of cystic fibrosis transmembrane conductance regulator: Production of a suitable protein for structural studies DOI : 10.1046/j.1432-1327.2000.01614.x
1999
Interest of colchicine for the treatment of cystic fibrosis patients. Preliminary report DOI : 10.1080/09629359990667
1998
Lithostathine, the presumed pancreatic stone inhibitor, does not interact specifically with calcium carbonate crystals DOI : 10.1074/jbc.273.9.4967
1997
Insight into cystic fibrosis by structural modelling of CFTR first nucleotide binding fold (NBF1) DOI : 10.1016/S0764-4469(97)85002-0
1997
Induction by antitumoral drugs of proteins that functionally complement CFTR: A novel therapy for cystic fibrosis? [2] DOI : 10.1016/S0140-6736(05)63510-6
1997
A novel model for the first nucleotide binding domain of the cystic fibrosis transmembrane conductance regulator DOI : 10.1016/S0014-5793(97)00363-3
1997
A new N-channel maximum entropy method in NMR for automatic reconstruction of "decoupled spectra" and J-coupling determination DOI : 10.1021/ci960321n
1996
What function for human lithostathine?: Structural investigations by three-dimensional structure modeling and high-resolution NMR spectroscopy DOI : 10.1093/protein/9.11.949
1994
Structure and stereochemistry of hazuntamine, a new bisindole alkaloid from Hazunta modesta var. Methuenii subvar. Methuenii DOI : 10.3987/com-93-6644
1990
Insight into protein nuclear magnetic resonance research DOI : 10.1016/0300-9084(90)90117-Y
1990
A d(GpG)‐platinated decanucleotide duplex is kinked: An extended NMR and molecular mechanics study DOI : 10.1111/j.1432-1033.1990.tb19435.x
1989
PARIS, a program for automatic recognition and integration of 2D NMR signals DOI : 10.1016/0022-2364(89)90177-7

Teaching

Elective Course Period (October and January)

Course Director

Synthetic Biology: An Introduction

Course Director

It will begin with a study of the building blocks of life and the ways in which information is transmitted within living systems. The course will then continue with an introduction to synthetic biology. This emerging discipline involves the engineering of biology—that is, the rational synthesis of complex systems based on or inspired by biology, offering new functionalities not found in nature. We will explore synthetic biology through the study of examples of achievements from recent research in this field.

Biotechnology (BIOTECH) track

Course Director

Some examples of workshops organized in recent years in 2A or 3A (lasting 3–5 days): Cloning and expression in microorganisms of proteins with applications in health and the environment (2A). Strategy for developing a cluster of startups and research institutions in Morocco in the field of agronomy (2A). Industry and research in the field of first-, second-, and third-generation biofuels (2A, 3A). Various medical imaging technologies, their development, and the related industries (3A). The role of bioinformatics in the development of new drugs (2A, 3A). The “medical technologies and medical devices” industry (3A). Biotechnological production of flavorings and colorants in the agri-food industry (3A). Mechanisms of innovation, commercialization, and entrepreneurship in biotechnology applied to healthcare (3A). The development of telemedicine and e-health (3A) Careers in strategic consulting for companies in the healthcare sector (3A) Some representative elective topics covered in recent years: Determination of the structure of human protein A in complex with ligands of therapeutic interest (Sanofi). Establishment and characterization of new in vitro and in vivo models of human melanomas from tumor samples (Pierre Fabre). Identification of peptide candidates for antitumor immunotherapy (CEA). Study of the expression of markers involved in pigmentation following UV exposure (L’Oréal). Ultrasound imaging of skeletal muscle (Généthon). Modeling of X-ray-nanoparticle interactions (Nanobiotix). Implementation (in C++) of an interface for a high-performance image analysis engine based on brain function (MIT, USA). Prediction of the pharmacokinetic properties of molecules using machine learning and symbolic prediction methods (Ariana Pharma). Development of a diagnostic platform (Genewave). Market study of rapid diagnostic tests in the cardiovascular field in Europe, the United States, and China (Aterovax). Increasing the effectiveness of sales forces (Ferring Pharmaceuticals). Methods for assessing the environmental impacts of advanced biofuel production pathways (Total). Identifying and evaluating technologies and processes for converting biomass into fermentable feedstocks (Total). Strategic evaluation of an enzymatic technology for treating pharmaceutical micropollutants (Da Volterra). Market analysis for innovative products from biotechnology companies (Alcimed). Study of the impact of hospital “marketing” on overall drug sales (Sanofi). Investing in the rare disease sector (Kurma Life Sciences Partners). Biotechnology companies: what role do they play in “Big Pharma” strategy? (Bionest Partners) Reducing material waste: controlling excess weight in finished products (LU). Logistics optimization within Danone Professional’s Customer Service department (Danone). Special Features of the Track: To ensure that students in this track benefit from a tailored and highly structured environment, the track has developed partnerships with various institutions where certain activities can take place: the Applied Microbiology Laboratory (CNRS, Gif-sur-Yvette), the Pasteur Institute (Paris), and the Department of Biochemistry and Biological Engineering (ENS-Cachan). The Biotechnology track welcomes both students motivated by the life sciences and their applications in biotechnology, as well as students interested in issues at the intersection of biology and other engineering disciplines (computer science, materials science, physics, processes, chemistry, etc.). Biotechnology is an industrial field closely linked to research and highly competitive. As such, it serves as a case study for understanding the mechanisms of innovation, its connection to research, and its commercialization within companies. Elective projects in the 3A year are “tailored” to align the topics with each student’s interests. Projects that straddle two elective areas are welcome and even encouraged.

Engineering and Health Research (Research Quarter)

Lecturer

PhD supervision

2025 Multimodal representations of protein structures oriented towards interaction prediction GERTNER Victor
2024 Systems biology and machine learning approaches for identifying therapeutic targets and their associated drugs. Application to the identification of new therapeutic strategies for cystic fibrosis. LAIGLE Victor
2024 RNA encoding using machine learning for drug discovery KARROUCHA Wissam
2022 Machine learning and systems biology to identify therapeutic strategies in atip3-deficient triple-negative breast cancer GUICHAOUA Gwenn
2021 Docking and Machine Learning approaches for exploring new "scaffolds" in the search for molecules of therapeutic interest. PINEL Philippe
2020 Computer study of the clinical and omic effects of CFTR modulators in cystic fibrosis and search for new targets CORNET Matthieu
2019 Search for new therapeutic targets for cystic fibrosis using systems biology and chemogenomics approaches NAJM Matthieu
2016 Detection of epistasis in genome-wide association studies using machine learning techniques for the identification of therapeutic targets SLIM Lotfi
2016 Genome-wide association studies guided by networks CLIMENTE GONZÁLEZ Héctor
2015 Statistical learning methods for virtual drug screening PLAYE Benoit