AI-powered multiscale modeling framework for ice cream formulation
An AI/ML-driven framework that integrates molecular dynamics to predict and optimize ice cream properties based on ingredients and processing, enhancing product development efficiency.
Overview
Prescience Insilico presents an innovative AI-powered multiscale modeling framework designed to revolutionize ice cream formulation. By integrating artificial intelligence and machine learning with molecular dynamics simulations, this framework predicts the physical, structural, and sensory properties of ice cream based on ingredient composition and process parameters. This enables product developers and food scientists to perform data-driven innovation, significantly reducing trial-and-error and enhancing R&D efficiency.
Technical specifications
The framework utilizes AI/ML models to predict key physical properties such as overrun, viscosity, melting rate, and fat globule size from specific ingredient and process inputs. Molecular dynamics simulations provide mechanistic insights into microstructure, studying fat-protein-water interactions at an atomic level. The software tool developed unifies data, models, and simulations into an accessible interface, offering a reverse engineering engine to recommend ideal formulations based on target sensory profiles. This approach allows for a comprehensive understanding of emulsion behavior and ice formation, aiding in the development of consumer-driven products.
Technology readiness level
Currently at TRL 6, the framework has been validated through extensive simulation design, execution, and model development. Future validation plans include literature reviews, dataset compilation, and further simulation executions. This ensures the framework is ready for integration and deployment in real-world ice cream formulation scenarios.
About Prescience Insilico
Prescience Insilico is a deep tech company that provides AI-driven software, research, and digital transformation services for the pharmaceutical and materials science industries. Its core software platform, PRinS3, integrates artificial intelligence, physics-based modeling, and high-performance computing to support drug discovery processes, including target identification, molecule design, virtual screening, and property prediction. The company also offers research services utilizing multiscale computational chemistry and develops custom digital solutions, such as digital twins, to enhance data management, collaboration, and the integration of machine learning models for materials and life science innovation.
These solutions enable partners to accelerate innovation and optimize research efficiency by addressing complex challenges in drug and materials development. By combining experimental data with advanced machine learning algorithms, the company helps organizations prioritize candidates with improved pharmacokinetic profiles, solubility, and bioavailability. The company’s approach is used to streamline preclinical drug development, improve treatment delivery systems, and support the design of innovative materials, catering to researchers and scientists in life sciences, healthcare, and specialized chemical sectors.
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