Orano Med is a biotechnology company owned by Orano. Our ambition is to develop a new generation of targeted cancer therapies using the unique properties of lead-212, a rare alpha-emitting radioisotope with highly promising medical properties.

Responsabilities

• In collaboration with the Discovery team, build and apply cheminformatics and computational chemistry capabilities to support the Design–Make–Test–Analyze (DMTA) discovery cycle in radiotheranostics.
• Contribute to the advancement of the radiotheranostic pipeline by applying computational approaches such as molecular docking, ligand-based design, diversity and conformational analyses, together with machine learning and AI methods, to support multiparameter optimization of radioligand candidates.
• Develop, optimize, and implement computational tools, workflows, and methods (e.g., docking, molecular dynamics, free-energy calculations, ML/AI-assisted design) to strengthen internal discovery capabilities and accelerate project progression.
• Provide computational support and scientific input across key discovery stages, including target assessment, hit identification, and lead optimization, working closely with medicinal chemistry and biology teams.
• Collaborate with the IT and data science teams to help integrate AI-enabled tools into day-to-day drug design workflows.
• Clearly communicate computational results, design hypotheses, and data-driven recommendations to multidisciplinary project teams.

For the time being, the position will be based in Chatillon, but in March the site is scheduled to move to Villejuif.

Requirements

• Ph.D. in Computational Chemistry, Theoretical Chemistry, Chemistry, or a related field, with industry and/or postdoctoral experience applying computational methods in drug discovery or radiopharmaceutical development

• Strong expertise in computational chemistry methods relevant to radiotheranostics—including molecular docking, virtual screening, molecular dynamics, FEP, homology modeling, QM, and pharmacophore modeling—and proficiency with open-source (e.g., OpenMM) and/or commercial platforms (e.g., Schrödinger, OpenEye, MOE) in an industry setting.

• Experience modeling peptides and small molecules, with solid knowledge of structure–activity relationships and multiparameter optimization for radioligand design (affinity, stability, clearance, selectivity). 

• Hands-on experience applying AI/ML approaches to molecular design and decision-making, including QSAR, property prediction, compound prioritization, diversity selection, and data-driven workflows integrated with physics-based methods.

• Proficiency in Python for data analysis, workflow automation, pipeline development, and deployment on HPC or cloud environments, including integration of AI/ML models into discovery workflows.

• Ability to collaborate in a multidisciplinary environment, translating computational and AI-driven insights into actionable design hypotheses with chemistry, biology, and radiochemistry teams.