The Role
AQEMIA is building a new generation of AI-driven drug discovery workflows, and this role is critical to scaling the speed and reliability of our pre-program screening operations. As Computational Chemist – Pre-Programs, you will own the end-to-end execution and monitoring of QEMI virtual screening and hit expansion campaigns, from structure selection and target preparation to active hit selection. This role exists to remove operational bottlenecks that currently depend on build-team capacity, enabling pre-programs to progress with greater autonomy, consistency, and throughput.
You will combine scientific judgment, operational ownership, and hands-on problem solving to ensure screening campaigns are delivered with high quality and reproducibility across all active pre-programs. Beyond execution, you will play a key role in identifying why runs succeed or fail and translating those learnings into actionable feedback for platform and product teams. Within 12 months, success in this role will mean a standardized, resilient, and scalable screening workflow that accelerates AQEMIA’s ability to identify high-quality hits across multiple discovery programs.
Own the end-to-end execution and monitoring of QEMI virtual screening and hit expansion campaigns across all active pre-programs
Select and prepare appropriate protein structures based on target biology, binding site context, and program objectives
Run and interpret screenability assessments, configure workflows, and coordinate screening execution with compute and engineering teams
Apply compound filtering and selection workflows, ensuring compounds meet registration, traceability, and screenability standards
Active contributor to pre-programs prioritization strategy by doing retrospectives of the screens, and identifying areas of improvement
Troubleshoot underperforming runs and identify structural, methodological, or operational improvements to increase hit quality and workflow reliability
Partner with engineering and platform teams to standardize, document, and progressively automate the run process as the platform evolves
Maintain robust operational documentation and backup processes to ensure continuity and resilience across team changes and scaling needs
Strong background in computational chemistry, cheminformatics, or computer-aided drug design (CADD)
Experience with structure-based virtual screening workflows, docking, scoring, and hit triage
Ability to independently assess protein structure quality and make scientific decisions on structure selection and screening strategy
Strong working knowledge of Python and scientific scripting workflows, including troubleshooting and modifying existing pipelines
Comfortable working in Linux environments and with command-line tools, configuration files, and automated workflows
Familiarity with chemical data handling and compound registration workflows, including SMILES and RDKit
Minimum 6 years of relevant experience in computational chemistry or related scientific fields
Nice‑to‑Have
Experience supporting early discovery or preclinical drug discovery programs in biotech or pharma
Familiarity with protein folding methods and structure prediction workflows
Understanding of medicinal chemistry concepts such as drug-likeness, developability, and ADMET
Experience contributing operational insights into platform or product improvement roadmaps
Prior experience scaling or standardising scientific workflows across multiple programs
AQEMIA is reinventing drug discovery with physics-based AI, enabling faster invention of new medicines for unmet needs (including oncology).
Key team members
Ana Manso Jones
Olivier Bezençon
Aurélie Legay
jean-philippe surivet
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