Israeli researchers have built a machine learning system that sifted through more than 50 million chemical compounds and produced 92 candidates for a new generation of sugar substitutes, the Hebrew University of Jerusalem announced on Sunday. The results were published in the journal Food Chemistry. According to Xinhua, the work shows how artificial intelligence and molecular modeling can speed up the search for new sweeteners while cutting down on the bitter or metallic aftertastes that have long troubled the industry.
Why it matters
Finding a sweetener that tastes clean is harder than it sounds. Many existing substitutes carry off-flavors that limit how much food and drink makers can use them, and the trial-and-error process of testing new molecules is slow and expensive. The Hebrew University approach flips that process: instead of testing compounds one at a time, the researchers used computers to filter a vast library of molecules before any laboratory work begins. Xinhua reports that the method could shorten the path from a promising molecule to a testable product.
The scale of the screening is the headline figure. The team applied its models to three chemical databases holding roughly 50.7 million molecules in total, a volume no conventional lab could work through by hand.
Teaching a model what sweet and bitter taste like
The foundation of the work was a pair of predictive models trained on compounds whose taste is already known. One model learned to recognize features associated with sweetness; the other learned to flag bitterness. With both running, the researchers could screen the databases for molecules predicted to taste sweet while discarding those likely to leave a bitter impression — the flaw that most often sinks a candidate sweetener.
That first pass produced a large pool of possibilities, which the team then refined. The screening ultimately yielded 556 potential sweeteners. From that group, the researchers selected 92 compounds judged to have the most favorable predicted properties and the best fit for future testing, according to Xinhua.
Docking the candidates onto the human sweet receptor
A second stage of the work looked at how the surviving molecules would actually interact with the body. The researchers used machine learning alongside molecular docking — a computational technique that simulates how a compound fits into a protein — to model binding to the single human sweet-taste receptor. The detailed structure of that receptor's component proteins was published last year, giving the team a physical target to work against.
Xinhua reports that many of the 92 selected compounds have chemical structures unlike those of sweeteners already on the market. That difference is significant: it suggests the screen may have opened routes to entirely new families of sugar alternatives rather than variations on existing ones.
What happens next will be the test of whether the predictions hold up. The 92 candidates were chosen for their suitability for future testing, and laboratory work will be needed to confirm that the modeled sweetness and receptor binding translate into real taste. Watch for follow-up studies that put these compounds through actual sensory trials — and for whether the same screening method gets applied to other taste problems, such as salt reduction or bitterness masking.
