ReviewFrontiers in pharmacology2026
AI-driven pilot platforms and computational pharmaceutics: accelerating innovation in small molecule drug development under industry 4.0 and 5.0 paradigms.
Review in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Artificial intelligence-driven digital twins in Pharma 4.0: transforming smart manufacturing, predictive quality assurance, and personalized drug delivery.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the era of artificial intelligence (AI) and Industry 4.0, pilot-scale platforms for small molecule chemical drugs are undergoing a transformative digital evolution. These platforms serve as a critical link between early-stage laboratory research and full-scale pharmaceutical manufacturing, ensuring process feasibility, scalability, and regulatory compliance. This review offers a comprehensive and forward-looking analysis of the structure, function, and strategic importance of pilot-scale systems within the modern pharmaceutical landscape. Focusing on the integration of AI and intelligent automation, the study highlights innovations such as AI-driven process optimization, predictive maintenance, data integration, digital twin technologies, and continuous manufacturing. These technologies are reshaping conventional production paradigms by enhancing efficiency, improving quality control, and reducing environmental impact. The convergence of computational pharmaceutics and green chemistry is also examined as a key driver of sustainable and intelligent drug development. Moreover, the review addresses the industry's transition toward Industry 5.0, characterized by human-machine collaboration, data-centric innovation, and an emphasis on sustainability. Persistent challenges such as equipment standardization gaps, data-sharing limitations, and outdated infrastructure are critically discussed. Drawing from industrial case studies, academic research, and best practices, this paper explores both the opportunities and constraints associated with AI-enabled pilot platforms. Ultimately, the review aims to inform future strategies in digital pharmaceutical manufacturing by underscoring the importance of technological innovation, regulatory alignment, and collaborative ecosystems in advancing the development, efficiency, and sustainability of small molecule drug production.
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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.