Quality Management Systems
ISO 9001 is the international standard for quality management systems. Here's how its principles guide our molecular design service, from your initial brief to the candidates and reports we deliver.
Why ISO 9001?
ISO 9001 provides a framework for delivering consistent quality and improving how work gets done. We apply its principles to our full molecular design service, using proprietary AI models, chemistry workflows, and simulations to develop candidates for lipid nanoparticles and protein degradation. Quality means having a clear process behind the molecules we deliver and evidence your team can review.
A Clear Brief from the Start
We begin by agreeing on the target, design goals, practical constraints, and deliverables with your team. That includes the available starting data, the properties you want to improve, and what we will check before a candidate reaches the final shortlist. These goals give us a shared basis for reviewing progress.
Checks at Every Design Stage
- Design and Chemistry: We review the starting structures, analyze relevant surfaces, run docking workflows, and refine the interactions that support each candidate.
- Simulation: We use molecular dynamics (MD), FEP, and MM-GBSA as needed to assess stability and binding energies. We review the results against the project goals and revisit designs that need more work.
Traceable Results
Each candidate is linked to the structures, workflow versions, and simulation settings used to assess it. As we refine a design, we keep a record of what changed and why. This makes it easier to review a result, investigate an unexpected finding, or return to an earlier design without losing the context.
A Useful Handover
We deliver a package of 20-50 optimized candidates, with the final scope agreed for your project. Alongside the molecular structures, we provide supporting simulation results, reports on key interactions and energies, and relevant molecular property data. We explain the remaining uncertainties so your team can plan synthesis and lab testing with a clear view of the evidence.
Learn from the Results
When your team shares experimental feedback, we compare it with our predictions and investigate the gaps. Those findings help us improve the next design cycle and the checks in our workflows. The aim is to make each round of work more useful to your research.