Senior Computational Chemist (Drug Discovery & Generative AI)

Senior Computational Chemist (Drug Discovery & Generative AI)

Pierre Fabre

Haute-Garonne, France

Your mission

• We are seeking a talented and highly motivated Senior Computational Chemist (Drug Discovery & Generative AI) who is willing to combine established industry level solutions with cutting-edge generative chemistry and AI-driven workflows for our Oncology pipeline.
• As a key member of the team, the candidate will work closely with medicinal chemists and data scientists ensuring that computational hypotheses and rational lead to the selection and synthesis of promising drug candidates.
• Your role within a pioneering company in full expansion:
• Project Leadership & Strategy: Actively guiding and executing the computational strategy for multiple drug discovery programs simultaneously.
• Integrated Molecular Modeling & Workflow Execution: Applying a hybrid approach that combines AI-driven generative workflows with physics-based simulations (MD, docking, QM, Binding Free Energy methods) to accelerate molecular design.
• Property Optimization: Utilizing QSAR models and ADME-Tox predictors to evaluate chemical series, ensuring that generative chemistry outputs align with critical drug-like properties.
• Infrastructure Navigation: Operating seamlessly across a hybrid technical environment, including Virtual Machines, and AWS cloud instances.
• Cross-Functional Collaboration: Partnering with chemists, biologists, and ADME experts to interpret experimental results and refine computational hypotheses in real-time.
• Collaborative Continuous Improvement: Partnering with team members to iteratively refine and optimize existing pipelines.

Who you are?

Your skills at the service of innovative projects:

• Education: PhD in Computational Chemistry, Molecular Modeling, Cheminformatics, or a related field with 5+ years of industry experience.
• Experience: 5+ years in a drug discovery environment with a proven track record of moving projects through the Lead Optimization phase.
• Modelling: Solid experience in Structure-Based (SBDD) and Ligand-Based (LBDD) design (e.g., Molecular Dynamics, FEP, Docking).
• Programming: Strong proficiency in Python for data manipulation and pipeline execution.
• Infrastructure Proficiency: Ability to navigate hybrid environments (Windows VMs, Linux, and AWS).
• Excellent written and oral communication skills in English
• Skilled in scripting within Unix/Linux environments and using command-line interfaces with high-performance computational clusters.
• Committed to good coding practices and adept at writing reproducible scripts within version control systems (e.g., Git).