This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Researchers Dr. Shayma El-Atawneh and professor Amiram Goldblum at the Hebrew University of Jerusalem have developed a new computational framework that identified 92 promising candidates for future sugar substitutes from more than 50 million chemical compounds.
The study, published in the journal Food Chemistry, demonstrates how artificial intelligence and molecular modeling could accelerate the discovery of new sweeteners while helping overcome one of the industry's persistent challenges: unwanted bitter aftertastes. As demand for low-calorie sugar alternatives grows, scientists are seeking sweeteners that provide pleasant taste along with favorable safety and stability profiles. Excessive sugar consumption has been linked to several health and environmental concerns.
High sugar intake can increase the risk of obesity, type 2 diabetes, fatty liver disease and cardiovascular disorders. The FDA has approved artificial sweeteners and established recommended daily amounts per kilogram for each. However, widely used artificial sweeteners such as aspartame, saccharin and sucralose have limitations, including bitter or metallic aftertastes.
Discovering new compounds that are sweet without undesirable characteristics remains a significant challenge. The researchers addressed this problem using a machine-learning algorithm called Iterative Stochastic Elimination (ISE), developed by Goldblum's research group. The models provided by ISE enable the screening and scoring of millions of compounds in silico in a short time, thereby greatly reducing the number of compounds for testing in vitro and in vivo.
The ISE method builds the models on the basis of molecular properties to predict whether a compound is likely to taste sweet or bitter. The researchers developed predictive models based on known sweet- and bitter-tasting compounds, enabling them to distinguish between desirable and undesirable taste characteristics. They then applied these models to screen three large chemical databases containing approximately 50.7 million molecules, identifying promising sweet candidates while filtering out compounds predicted to be bitter.
To refine their findings, the team combined machine learning with molecular docking, a computational technique that simulates how molecules interact with biological targets. In this case, the researchers examined how candidate compounds might bind to the single human sweet-taste receptor, which is composed of two protein subunits, T1R2 and T1R3. The detailed structure of this receptor was published only last year, enabling refinement by docking.
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