M2 internship - In silico

**Workplace:** Pharmacy department of UFR3S, University of Lille **Duration:** 6 months starting from January/February 2027 (at the beginning of the second semester of M2 courses) **Context:** The internship will be conducted in the Inserm Unit U1286 INFINITE, a translational research unit dedicated to inflammation, from its origins to the patient (https://www.lille-inflammation-research.org/). Built on long-standing expertise in inflammatory bowel disease (ulcerative colitis and Crohn's disease), INFINITE brings together 120 researchers and clinicians. One of the team research axes is dedicated to therapeutic innovation targeting inflammation, with a medicinal chemistry team that compasses all disciplines: chemistry, in silico modelling, and biology. One of the research topics is the protein GPR35, an orphan G protein-coupled receptor that has become a major therapeutic target in inflammatory bowel diseases. The receptor plays a dual role. Some findings point to a pro-inflammatory contribution in immune cells, but most of the literature describes a protective function in the gastrointestinal tract, where GPR35 activation promotes mucosal repair. Suppressing this basal activity with an inverse agonist increases colonic barrier permeability. Targeting GPR35 with agonists, rather than inverse agonists, is therefore a promising strategy where mucosal barrier function is compromised. Our objective is to elucidate the atomic-level pharmacology of GPR35 and to identify new selective agonists, ideally biased toward the anti-inflammatory β-arrestin-2 pathway. The student will not start from scratch, and will work within the In silico Lille research group (https://insilicolille.fr/). Already available in our lab: a virtual screen of around one million commercial compounds, initial classical molecular dynamics simulations, and conformations of GPR35 alone, in complex with G13, and in complex with β-arrestin-2. Our biology group handles experimental validation of the selected compounds. **Position and assignments:** The current bottleneck is not generating candidates but ranking them rationally and exploring a far larger chemical space without prohibitive computational cost. The internship consists in building that selection pipeline, from billions of available molecules down to around ten compounds to order and test. 1. **Develop a complete deep docking protocol** . Deep docking works by docking a representative sample of a chemical library, training a machine learning model on the resulting scores, and then using that model to iteratively discard the molecules that stand no chance of ranking well. Here, the objective is to reproduce and benchmark the existing screen against known GPR35 agonists, build the iteration loop (sample docking, training, prediction, filtering), then apply it to an ultra-large library, and quantify both the speed-up and any loss of enrichment. 2. **Establish a hit selection procedure** . Deliver a written, justified and reproducible procedure rather than a raw ranking: combined docking scores, physics-based rescoring, physicochemical and ADMET filters, chemical diversity analysis, interaction fingerprints, and visual inspection of poses. 3. **Implement binding free energy calculations** . Set up relative binding free energy (RBFE) calculations with the open-source OpenFE suite, validate the method on a series of agonists with known activity, then apply it to the final ranking of shortlisted candidates. 4. **Depending on progress and on the candidate's interests, molecular dynamics** , to characterise the stability of the best complexes by molecular dynamics, or exploit the available G13- and β-arrestin-2-bound conformations to derive an interaction signature associated with signalling bias, usable in turn as a selection criterion **Expected deliverable:** a prioritised list of compounds for experimental testing, together with the documented, version-controlled pipeline that produced it. **Candidate profile:** Master 2 student in chemoinformatics, structural bioinformatics, molecular modelling, theoretical chemistry, computer science applied to life sciences, or drug discovery (chemistry and/or biology) who is interested in getting involved in the in silico research. -         Multidisciplinary profiles are especially welcome. An interest in biology, at minimum to understand the assays run by our group, ideally to take part in interpreting them, is a significant plus for us. -         Feel comfortable working with computers, both Windows and Linux, with interest in programming is a plus. -         No prior experience with deep docking or free energy calculations is expected: these methods will be learned during the internship. -         Methodological rigour, autonomy, and a habit of documenting your work -         This internship is intended as an entry point to a PhD project for the 2026 research grants of the Graduate School ”Biology and Health” in Lille. An interest in pursuing a PhD on this project is a plus, though not a requirement. **Application:** Send a CV, cover letter, along few lines explaining what interests you in this project to [email protected] with as subject “M2 internship GPR35”. Applications are accepted until November 15th, 2026.