Application of Molecular Simulation and Machine Learning in Consumer Packaged Goods
Introduction
Schrödinger offers powerful, easy-to-use solutions for CPG product research and development. Designed for non-expert and expert users alike, the Schrödinger platform offers simple workflows for building, simulating and analyzing real-life systems with advanced physics-based modeling and machine learning technology. Schrödinger’s team of dedicated Scientific and Technology Support experts bring domain-level expertise to ensure users gain the most project impact from the platform. Additional research services and collaboration opportunities are available. Here we present example applications of Schrödinger’s Consumer Packaged Goods capabilities.
Food & Beverage
Advance food processing and bio-based packaging
Predict water uptake, transport and plasticization in starch
Extend product shelf life
Predict chemical stability and understand antifungal mechanism of edible coating
Incorporate plant based-protein ingredients
Understand aggregation and emulsifying properties and mechanism for emulsions and foam
Predict and design flavors and scents
Understand taste mechanisms with deep learning and informatics-based methods
Understand food emulsions and architecture
Predict morphology of complex emulsions
Cosmetic and Personal Care
Optimize shampoo formulations
Model and quantify interface interactions between F-layers of hair follicles and shampoo formulations
Replace traditional surfactants
Simulate micelle formation, morphology, and self assembly of emulsifier into micelles
Understand microemulsions
Explore phase diagrams, multi-component separation/aggregation of microemulsion systems
Drive formulation development
Perform virtual screening of surfactant systems
Build realistic models for complex formulations
Construct mixtures of multiple ingredients, encapsulating polymers, and additives
Cleaning Products
Improve cleaning efficiency
Understand self assembly and morphology of cleaning surfactants on surfaces and the effect of environment (e.g. temperature) on the changes of efficiency
Understand micelle formation
Predict self assembly of surfactants
Understand microemulsions
Provide insight to structure, interfacial properties, and thermodynamics
Advance formulation stability
Predict stability, decomposition, and spectroscopic properties of molecules (e.g antioxidants)
Packaging
Develop sustainable packaging
Study interfacial interactions between packaging materials and consumer goods, and simulate water uptake for barrier design and performance
Minimize production waste
Predict thermomechanical properties of packaging materials
Innovate with natural materials
Explore active, recyclable, and bio-based materials through molecular simulation
Reduce processing cost
Investigate the ability to scale to manufacturing-level processes
Team Collaboration and Digital Data Management
Empower collaboration
Employ enterprise informatics tools for sharing experimental and predictive models seamlessly
Amplify research
Rapid deployment of machine learning models to drive predictions
Promote project management
Work side by side, accelerating project communication and collective learning; screen, share results, and make informed decisions
Products
Empower collaboration
Check on the products that enable your success in the CPG industry.
Download our Materials Science Product Guide.
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Selected publications
Molecular-Level Examination of Amorphous Solid Dispersion Dissolution
Mohammad Atif Faiz Afzal, Kristin Lehmkemper, Ekaterina Sobich, Thomas F. Hughes, David J. Giesen, Teng Zhang, Caroline M. Krauter, Paul Winget, Matthias Degenhardt, Samuel O. Kyeremateng*, Andrea R. Browning, and John C. Shelley*
Mol. Pharmaceutics 2021, 18, 11, 3999-4014
The structural basis of odorant recognition in insect olfactory receptors
Josefina del Mármol, Mackenzie A. Yedlin & Vanessa Ruta
Nature 2021, 597, 126-131
Characterizing moisture uptake and plasticization effects of water on amorphous amylose starch models using molecular dynamics methods
Jeffrey M.Sanders, Mayank Misra, Thomas J.L.Mustard, David J.Giesen, Teng Zhang, John Shelley, Mathew D.Halls
Carbohydrate Polymers 2021, 252, 11716
Comprehensive structure-activity-relationship studies of sensory active compounds in licorice (Glycyrrhiza glabra)
Christian Schmid, Anne Brockhoff, Yaron Ben Shoshan-Galeczki, Maximilian Kranz, Timo D. Stark, Rukiye Erkaya, Wolfgang Meyerhof, Masha Y. Niv, Corinna Dawid, Thomas Hofmann
Food Chemistry 2021, 364, 130420
In Silico Investigation of Bitter Hop-Derived Compounds and Their Cognate Bitter Taste Receptors
Andreas Dunkel, Thomas Hofmann, and Antonella Di Pizio
J. Agric. Food Chem. 2020, 68, 38, 10414-10423
Structure-based screening for discovery of sweet compounds
Yaron Ben Shoshan-Galeczki, Masha Y Niv
Food Chemistry 2020, 315, 126286
Bitter or not? BitterPredict, a tool for predicting taste from chemical structure
Dagan-Wiener, A.; Nissim, I.; Ben Abu, N.; Borgonovo, G.; Bassoli, A.; Niv, M.Y.
Scientific Reports 2017, 7, 12074
Molecular Dynamics Simulation Study of Sodium Dodecyl Sulfate Micelle: Water Penetration and Sodium Dodecyl Sulfate Dissociation
Chun, B.J.; Choi, J.I.; Jang, S.S.
Colloids and Surfaces A: Physicochemical and Engineering Aspects 2015, 474, 36