Schrödinger is excited to be participating in the IFT First conference taking place on July 16th-19th in Chicago, Illinois. Join us for a presentation by Jeff Sanders, Product Manager at Schrödinger titled, "Building a Sustainable Future for Food: The Role of Molecular Simulation and Machine Learning". Stop by booth S4167 to speak with Schrödinger scientists.

Speaker: Jeff Sanders, Product Manager
Date/Time: July 18 | 2:00-2:30pm CST
Location: McCormick Place, South Hall Booth S3083
Abstract:
As the food and beverage industry undergoes dynamic shifts in consumer preferences, the demand for novel and innovative products is skyrocketing. Modern customers are increasingly discerning, considering factors such as ingredient knowledge, food origin, and optimal choices for a healthy lifestyle. These concerns shed light on existing weaknesses in food research and development, characterized by a lack of innovation and reliance on incremental improvements. To thrive in the consumer marketplace, it is imperative to develop new food and drink formulations.
To address these challenges and maintain a competitive edge, a comprehensive understanding of ingredient behavior within products is crucial, not only for new product development but also for end-to-end traceability. Streamlining this process can be achieved through the application of multi-scale physics simulations, reducing product development costs and optimizing large-scale production. Molecular simulation techniques offer a unique opportunity to predict how individual ingredients interact within formulations. By employing atomistic simulations, researchers and engineers can unravel critical insights into product morphology, solubility, and other physical properties, given only the knowledge of the components. Unlike process simulations, which require many inputs that are often not known, molecular models comprising millions of atoms can be constructed based solely on chemistry and composition, enabling accurate property predictions.
Beyond physics-based modeling, chemical information can be leveraged to develop machine learning models that incorporate existing experimental or sensory data. This integrated approach allows for a more comprehensive understanding of flavors and ingredients compared to simulation alone. Join us in this talk as we delve into the cutting-edge advancements in molecular modeling techniques tailored for flavors and ingredient research, propelling product innovation in the food and beverage industry.
 

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