ReviewPharmaceutics2026
Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.
Review in Pharmaceutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Supramolecular inclusion complexes are widely used in drug delivery and other fields, with the advantages of controllable structures, high stability, excellent biocompatibility, and the ability to improve drug solubility and achieve controlled release. However, traditional screening methods rely on experimental trial and error, which suffer from long cycles, high costs, and low throughput, limiting research and development efficiency. In recent years, the development of artificial intelligence has provided new solutions for the screening of inclusion complexes. This paper systematically reviewed the core technological system of AI in the screening of inclusion complexes, focusing on two aspects: prediction and optimization of key properties and rational design of host molecules, summarizing their specific application progress. At the same time, we analyzed the current core challenges, including data scarcity, insufficient model interpretability, and limited generalization capabilities, and propose future development directions such as building standardized databases, integrating physicochemical principles (e.g., molecular mechanics and thermodynamics), and establishing closed-loop research and development platforms. This review aims to provide a systematic reference for the in-depth application of artificial intelligence in the field of supramolecular inclusion complexes.
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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.