Ai-guided formulation optimization for shelf life extension

Overview

The AI-guided formulation optimization solution leverages a combination of chemistry-informed machine learning (ML) and physics-based modeling to revolutionize the development of formulations with extended shelf lives. By establishing robust relationships between ingredient structures, compositions, and processing conditions, this approach accelerates formulation development. It utilizes physics-based modeling to provide insights into mixture interactions that influence shelf life, offering a reduction in trial-and-error experimentation and a clear pathway to optimized formulations.

Technical specifications

Key features:

• Integration of ML models with physics-based modeling to predict and optimize shelf life properties.
• Use of Schrödinger’s automated Formulation ML solution for accurate shelf life predictions based on a curated dataset.
• Deployment of molecular dynamics simulations to capture critical ingredient interactions, enhancing model accuracy.
• Optimization algorithms to systematically identify superior ingredient combinations and compositions for desired shelf life.
• Multiparameter optimization approaches for formulations with multiple target criteria.

Technology readiness level

This solution has reached Technology Readiness Level 9, indicating it is fully validated and ready for commercial deployment. The approach has been successfully applied in extending the shelf life of commercial food products, demonstrating its efficacy and practical application in the industry.