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Machine learning (ML) is revolutionizing formulation design by enabling data-driven predictions of critical performance indicators, such as solubility, viscosity, stability, and even sensory properties. Chemistry-informed AI/ML models provide a powerful framework for accelerating innovation across a wide range of formulations � from personal care and food, to pharma and battery electrolytes. By analyzing large, diverse datasets, ML can predict the behavior of new formulations, including complex mixtures and ingredients that are combinations of multiple mixtures, dramatically reducing reliance on trial-and-error approaches and speeding time-to-market.

Automated solutions can integrate ingredient composition and molecular structure to generate predictive models that optimize formulation performance. This empowers R&D teams to explore complex formulation spaces, reduce development cycles, and innovate more effectively. In this webinar, we will demonstrate how Schrödinger’s integrated ML- and physics-based approaches are transforming formulation design, with an emphasis on applications relevant to consumer packaged goods (CPG).