QUAI is GeneOn’s research execution framework that adapts design and optimization strategies per disease and target.
It is not a single “drug program” but an engine that combines AI-driven generation and quantum-inspired optimization
into an auditable workflow, producing experiment-ready shortlists.
QUAI is designed for decision-making: each step outputs interpretable artifacts, not only model scores.
Constraint-aware candidate generation for small molecules or peptides, optimized for downstream validation.
Geometry-aware docking and optional AI rescoring to reduce false positives and refine ranking.
Physics-based validation of stability and interaction persistence to support Go/No-Go decisions.
QUAI is structured as an auditable workflow. Each phase produces outputs that can be reviewed by scientists
and directly used in the next stage.
Select target mechanism, binding site / hotspot, exclusion rules, and assay constraints.
Generate candidate libraries aligned to modality and mechanism (molecule/peptide).
Docking-based selection and MD-based validation gates to remove unstable or infeasible candidates.
Provide experiment-ready shortlists with traceable rationale and structured deliverables.
AI-driven design with quantum-inspired optimization to support mechanism-aligned candidate refinement.
Strategy switches to match target biology and modality needs; optimization modules can be inverted per project.
QUAI’s differentiator is not “one algorithm,” but the ability to reconfigure design and optimization depending on
disease context and target constraints.
Share your target, modality preference (small molecule / peptide), and constraints. GeneOn will propose a scoped
QUAI execution plan with deliverables aligned to docking and MD validation.
Tip: If you already have a binding site/hotspot definition, include it. If not, QUAI can start from structure discovery.
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