Artificial intelligence-assisted identification and screening strategies in sweetener design
Bibliographic record
Abstract
The burgeoning consumer demand for healthier and sustainable alternatives to conventional sugars has catalyzed significant innovation for the design of artificial sweeteners. This critical review delves into the transformative role of artificial intelligence (AI) for the research and development of novel sweeteners, offering a multifaceted analysis of the intersection between AI and sweetener design. The review traverses the spectrum of AI applications, and emphasizes critical role of AI in virtual screening, especially in relation to the structures of sweet taste receptor. The synergy between molecular dynamics simulation and structure-based virtual screening (SBVS) is spotlighted as a key strategy to bolster the efficiency and precision in the identification of potential sweeteners. Moreover, the review dedicates the utilization of AI-driven strategies within the realm of quantitative structure-activity relationship (QSAR) modeling, revealing groundbreaking methods that eclipse conventional techniques. The use of AI can predict the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiles of sweeteners, a crucial component in fully comprehending their pharmacokinetic behaviors. This review highlights the transformational effect of AI on the development and screening of sweeteners, introducing groundbreaking perspectives and techniques poised to dramatically transform the domains of the food and pharmaceutical industries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".