Senior Computational Chemist - AI-Driven Drug Design Remote

Aqemia; Paris; Hybrid; EUR 120,000 - 180,000; Full time

Benefits offered by this job

Flexible work arrangement (up to 2 rem

Paris and London offices

Job summary

AQEMIA in Paris and London is seeking a high-impact scientist to drive computer-aided design of novel small molecules. You will integrate physics-based modeling, generative AI, and medicinal chemistry to guide decisions across internal programs and pharma partnerships.

You will work with chemists, biologists, physicists, and ML scientists to push early drug discovery from hit identification to candidate nomination, while evolving AI-assisted design methods and workflows.

Qualifications

• PhD or Master's with strong industry depth in drug discovery.
• Experience in small molecule design across hit-to-lead and lead optimization.
• Proficiency in Python and Linux; ability to run and adapt computational workflows.

Responsibilities

• Design, prioritize and optimize small molecules across programs.
• Own the design hypothesis and adapt plans as data evolves.
• Apply docking, MD, free energy methods and chemoinformatics in design decisions.
• Integrate biochemical, biophysical and ADME-Tox data to guide experiments.
• Pilot new AI-enabled design workflows within live projects.

Skills

Structure-based design

Ligand-based design

Protein-ligand interactions

Python scripting

Linux

Evidence-driven communication

Education

PhD in computational chemistry/medicinal chemistry/chemical biology/biophysics or related field

Master’s in a relevant field with equivalent industry depth

Tools

RDKit

OpenMM

AmberTools

GROMACS

Job description

AQEMIA in Paris and London is seeking a high-impact scientist to drive computer-aided design of novel small molecules. You will integrate physics-based modeling, generative AI, and medicinal chemistry to guide decisions across internal programs and pharma partnerships.

You will work with chemists, biologists, physicists, and ML scientists to push early drug discovery from hit identification to candidate nomination, while evolving AI-assisted design methods and workflows.