InventU Sustainable Future Congress

From Natural Ingredients to Packaging: Computational Strategies for Sustainable Personal Care Products

Speaker:
Jeff Sanders, Research Leader, Schrödinger

Abstract:
Research and development in cosmetic and personal care products increasingly face sustainability-driven challenges, including reducing development time and resource consumption while limiting reliance on new or scarce raw materials. To address these constraints, predictive modeling, formulation machine learning, and natural product ingredient characterization provide complementary approaches to accelerate sustainable innovation. Formulation machine-learning models leverage existing experimental and performance data to optimize ingredient combinations, reduce redundant testing, and guide reformulation toward more sustainable and bio-based alternatives. Computational chemistry methods enable molecular-level characterization of natural ingredients, improving understanding of their structural diversity, physicochemical properties, and stability within complex formulations. These tools also provide insight into formulation morphology, interactions with biological surfaces such as skin and hair, and product–packaging interactions that influence shelf-life and material compatibility. Through representative case studies, we demonstrate how integrating formulation ML with physics-based simulations reduces trial-and-error experimentation, maximizes the value of existing data, and supports the design of high-performance, sustainable cosmetic products from formulation through packaging and end use.