Senior Computational Chemist (Drug Discovery & Generative AI)

2w2 weeks ago

Pierre Fabre Group

Toulouse, FR · Full-time · €65,000 – €95,000

About this role

Pierre Fabre is the 2nd largest dermo-cosmetics laboratory in the world and the 2nd largest private French pharmaceutical group. We are seeking a talented Senior Computational Chemist to combine established industry solutions with cutting-edge generative chemistry and AI-driven workflows for our Oncology pipeline. The candidate will work closely with medicinal chemists and data scientists to select promising drug candidates.

As a key team member, guide and execute computational strategy across multiple drug discovery programs. Apply hybrid approaches blending AI-driven generative workflows with physics-based simulations like MD, docking, QM, and Binding Free Energy methods. Accelerate molecular design while optimizing properties using QSAR models and ADME-Tox predictors.

Operate across hybrid environments including Virtual Machines and AWS cloud instances. Partner with chemists, biologists, and ADME experts to interpret results and refine hypotheses in real-time. Collaborate to iteratively improve pipelines and ensure alignment with drug-like properties.

This position is based in Toulouse compatible with teleworking up to 2 days a week. Join a pioneering company recognized as one of World's Best Employers 2021 by Forbes. Benefit from an attractive remuneration package in a socially responsible group.

Requirements

Expertise in combining established computational chemistry solutions with generative chemistry and AI-driven workflows
Proficiency in physics-based simulations including MD, docking, QM, and Binding Free Energy methods
Experience with QSAR models and ADME-Tox predictors for property optimization
Ability to navigate hybrid computing environments like Virtual Machines and AWS
Strong skills in molecular modeling for drug discovery in oncology
Background in integrating computational hypotheses with medicinal chemistry synthesis

Responsibilities

Actively guiding and executing the computational strategy for multiple drug discovery programs simultaneously
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
Utilizing QSAR models and ADME-Tox predictors to evaluate chemical series and align generative outputs with drug-like properties
Operating seamlessly across a hybrid technical environment, including Virtual Machines and AWS cloud instances
Partnering with chemists, biologists, and ADME experts to interpret experimental results and refine computational hypotheses in real-time
Partnering with team members to iteratively refine and optimize existing pipelines