ReviewChirality2026
Rethinking Chiral Decision-Making in Pharmaceutical Analysis: The Emerging Role of Artificial Intelligence.
Review in Chirality, 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chirality remains a central yet inconsistently operationalized concept in pharmaceutical analysis. Although regulatory frameworks emphasize stereochemical characterization and enantiomeric control, practical analytical decisions are often guided by expert intuition and historical precedent. These include determining when chiral separation is necessary, when achiral methods are sufficient, and how to balance analytical rigor with analytical complexity. With increasing molecular complexity, accelerated development timelines, and heightened regulatory expectations, such intuition-driven approaches face growing limitations. Artificial intelligence (AI) has emerged as a powerful enabler across pharmaceutical sciences; however, its role in chiral decision-making has not been systematically articulated. This perspective reframes chirality as a decision-driven discipline and proposes a concept-driven, framework-oriented approach in which AI functions as a cognitive decision-support layer rather than a replacement for stereochemical expertise. It integrates stereochemistry, decision science, explainable artificial intelligence (XAI), and regulatory reasoning into a coherent approach for chiral decision-making in pharmaceutical analysis. Unlike existing AI applications that primarily focus on analytical optimization, this perspective proposes a decision-oriented framework for evaluating when chiral analysis is warranted and how analytical decisions may be supported using multidisciplinary evidence. The proposed framework bridges traditional stereochemical reasoning with data-driven intelligence and provides a structured basis for future chiral decision-support systems. As a framework-oriented perspective, this work is intended to stimulate discussion and guide future empirical evaluation, including prototype decision-support systems, curated datasets, and prospective validation studies, rather than present a validated AI implementation.
Indexed as
Identifiers
What 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.