ReviewMini reviews in medicinal chemistry2026
The Use of Artificial Intelligence in the Formulation of Effervescent Tablets: A Review.
Review in Mini reviews in medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Phytochemical Diversity, Mechanistic Pharmacology, and Therapeutic Potential ofFoods (Basel, Switzerland) · 2026Review
- Chemistry of Materials and Emerging Techniques: Future of Drug Discovery and Medicinal Product Developments.Mini reviews in medicinal chemistry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) is emerging as a valuable tool in pharmaceutical formulations, including the development of effervescent tablets (ETs). This review highlights how AI techniques are being explored to support ET formulation designs, optimize component ratios, predict disintegration and dissolution behavior, and control reactions through artificial neural networks, support vector machines, and machine learning. These techniques have been applied in recent studies to enhance stability, improve disintegration times, and flavor masking. Computational fluid dynamics simulations of effervescence and dissolution are underexplored for ETs. Data-driven approaches, like response surface modeling, require ingredient concentrations, tablet properties, consumer preferences, and predictive analytics for optimization. However, limited comprehensive datasets, complex reactions, environmental sensitivities, and ethical/regulatory considerations pose challenges. Overcoming these obstacles, as identified in the current literature, could enable AI to innovate ET development and personalization.
Indexed as
Identifiers
40908692What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.