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🧬 Master 2 Internship in Accelerated Virtual Screening | University of Lille – Inserm U1286 INFINITE
Institution: University of Lille – UFR3S, Pharmacy Department
Research Unit: Inserm Unit U1286 INFINITE – In Silico Lille Research Group
Supervisor: Corentin Bedart
📍 Location: Lille, France
💼 Position Available: Master 2 Internship
🔬 Research Area: Accelerated Virtual Screening | GPR35 Receptor | Computational Drug Discovery | Molecular Modeling | Deep Docking | Free-Energy Calculations | Molecular Dynamics | Inflammatory Bowel Disease
The Pharmacy Department of UFR3S at the University of Lille is offering a 6-month Master 2 internship focused on the accelerated virtual screening of the GPR35 receptor for inflammatory bowel disease.
The internship will be conducted within Inserm Unit U1286 INFINITE, a translational research unit focused on inflammatory diseases, in collaboration with the In Silico Lille research group.
🔬 Research Background
The research project focuses on GPR35, an orphan G protein-coupled receptor (GPCR) that has emerged as a potential therapeutic target for inflammatory bowel diseases, including ulcerative colitis and Crohn’s disease.
The project aims to investigate the molecular pharmacology of GPR35 and identify new selective agonists, ideally biased toward the anti-inflammatory β-arrestin-2 pathway.
The research group already has computational resources and structural models available, including:
- Virtual screening of approximately one million compounds
- Classical molecular dynamics simulations
- GPR35 structures in different conformational states
- GPR35 in complex with G13
- GPR35 in complex with β-arrestin-2
- Experimental biological validation of selected compounds
The successful candidate will therefore work within an established computational drug discovery workflow rather than starting the project from scratch.
🧪 Internship Research Focus
The main objective is to develop a rational and computationally efficient compound selection pipeline, allowing millions of molecules to be reduced to approximately ten experimentally testable compounds.
The project will focus on:
- Accelerated virtual screening
- Deep docking
- Machine-learning-assisted molecular screening
- Molecular docking
- Hit selection and prioritization
- Binding free-energy calculations
- GPR35 agonist discovery
- Molecular dynamics simulations
- Molecular recognition and protein–ligand interactions
- Biased signaling toward the β-arrestin-2 pathway
🔬 Key Research Assignments
1. Develop a Complete Deep Docking Protocol
Develop a deep docking workflow by docking representative samples of a chemical library, training a machine-learning model on the resulting scores, and using the model to iteratively eliminate molecules unlikely to rank well.
The workflow will include:
- Sample docking
- Machine-learning model training
- Prediction
- Filtering
- Iterative screening
- Validation against known GPR35 agonists
- Application to an ultra-large chemical library
The objective is to reproduce and benchmark existing screening results while improving speed and enrichment.
2. Establish a Hit Selection Procedure
Develop a written, justified, and reproducible procedure for selecting promising compounds.
The selection process will combine:
- Docking scores
- Physics-based rescoring
- Physicochemical filters
- ADMET filters
- Chemical diversity analysis
- Interaction fingerprints
- Visual inspection of binding poses
3. Implement Binding Free-Energy Calculations
Set up relative binding free-energy (RBFE) calculations using the open-source OpenFE suite.
The candidate will:
- Establish the RBFE workflow
- Validate the methodology using agonists with known activity
- Apply the workflow to shortlisted candidates
- Incorporate free-energy calculations into the final ranking process
4. Molecular Dynamics Simulations
Depending on project progress and the candidate’s interests, molecular dynamics simulations may be performed to characterize the stability of the best complexes.
The work may also explore available G13- and β-arrestin-2-bound conformations to investigate interaction fingerprints associated with signaling bias.
🎯 Expected Deliverable
The expected outcome is a prioritized list of compounds for experimental testing, together with a documented and version-controlled computational pipeline used to generate the final ranking.
👨🎓 Ideal Candidates
The internship is intended for Master 2 students with a background in:
- Cheminformatics
- Structural Bioinformatics
- Molecular Modeling
- Theoretical Chemistry
- Computer Science Applied to Life Sciences
- Drug Discovery
- Chemistry
- Biology
- Related computational disciplines
Candidates should have:
- An interest in in silico research and computational drug discovery.
- An interest in biology sufficient to understand the experimental assays used by the research group.
- Comfort working with computers, including Windows and Linux environments.
- Interest in programming and computational methods.
- Methodological rigor and autonomy.
- A habit of carefully documenting research work.
Previous experience with deep docking or free-energy calculations is not required, as these methods will be learned during the internship.
Multidisciplinary profiles are particularly welcome.
💻 Computational Methods & Tools
The internship provides exposure to:
- Deep Docking
- Molecular Docking
- Machine Learning
- Molecular Dynamics
- Binding Free-Energy Calculations
- Relative Binding Free Energy (RBFE)
- OpenFE
- ADMET Filtering
- Physics-Based Rescoring
- Interaction Fingerprints
- Chemical Diversity Analysis
- Protein–Ligand Interaction Analysis
- Computational Drug Discovery
- Large-Scale Virtual Screening
🌟 What You’ll Gain
The selected M2 intern will have the opportunity to:
- Work on an active computational drug discovery project.
- Develop expertise in accelerated virtual screening.
- Learn and implement deep docking methodologies.
- Gain hands-on experience with machine-learning-assisted screening.
- Learn binding free-energy calculations using OpenFE.
- Work with molecular dynamics simulations.
- Study GPCR–ligand interactions and molecular recognition.
- Contribute to the discovery of potential GPR35 agonists.
- Work within an interdisciplinary in silico and experimental research environment.
- Develop a documented and reproducible computational workflow.
The internship is also intended as a potential entry point to a PhD project, with the research project planned within the framework of the 2026 research grants of the Graduate School “Biology and Health” in Lille.
An interest in pursuing a PhD is considered a plus but is not a requirement.
📅 Internship Details
Institution: University of Lille
Department: Pharmacy Department, UFR3S
Research Unit: Inserm Unit U1286 INFINITE
Research Group: In Silico Lille
Supervisor: Corentin Bedart
Position: Master 2 Internship
Location: Lille, France
Duration: 6 Months
Start Date: January/February 2027
Research Focus: Accelerated Virtual Screening of GPR35 for Inflammatory Bowel Disease
Key Methods: Deep Docking | Molecular Docking | Machine Learning | OpenFE | RBFE | Molecular Dynamics
Target: GPR35 Receptor
Therapeutic Area: Inflammatory Bowel Disease
Preferred Background: Cheminformatics | Structural Bioinformatics | Molecular Modeling | Theoretical Chemistry | Computer Science Applied to Life Sciences | Drug Discovery
Operating Systems: Windows & Linux
Prior Deep Docking Experience: Not Required
PhD Opportunity: Potential pathway to a PhD project
Application Deadline: 15 November 2026
📩 How to Apply
Interested candidates should submit:
- CV
- Cover Letter
- A few lines explaining what interests you about the project
📧 Application Email
Email: corentin.bedart@univ-lille.fr
Subject: “M2 internship GPR35”
Applications will be accepted until 15 November 2026.
Category
Master 2 Internship | Computational Drug Discovery | Virtual Screening | Deep Docking | Molecular Modeling | Free-Energy Calculations | GPR35 | GPCR | Inflammatory Bowel Disease | University of Lille | Inserm | France Internships | Research Internships | Drug Discovery
🏷️ Tags
#M2Internship #InternshipFrance #UniversityOfLille #Inserm #ComputationalDrugDiscovery #VirtualScreening #DeepDocking #MolecularDocking
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