A graph-learning workflow to recommend compatible alternatives when key cosmetic ingredients are unavailable.
Substitution decisions need to be both fast and technically reliable, but expert screening can be time-consuming.
The model ranks candidates based on structural and functional proximity, helping teams pre-select substitutes before lab validation.
Key outcome
Shorter substitution cycles and improved pre-screening quality for formulation teams.