ArticleNature communications2024
In silico formulation optimization and particle engineering of pharmaceutical products using a generative artificial intelligence structure synthesis method.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed.
- Mitigating the Resolution-Field of View Trade-Off for Comprehensive Microstructural Characterization with Advanced AI Methods.AAPS PharmSciTech · 2026Article
- High-throughput microfluidics and machine learning-assisted screening of lipid nanoparticle formulations for siRNA delivery.Materials today. Bio · 2026Article
- Nanomaterials for the Prevention, Detection, and Treatment of Pharyngeal Human Papillomavirus Infection: A Translational Roadmap.Materials (Basel, Switzerland) · 2026Review
- Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study.Journal of cheminformatics · 2026Article
- Accelerated drug development using a digital formulator and a self-driving tableting data factory.Nature communications · 2026Article
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- AI-enabled, QbD-aligned Predictive, and Sustainable Design of Natural Polymer-based Drug Delivery Systems.AAPS PharmSciTech · 2026Review
- Mechanism-informed machine learning for interpretable drug release prediction and virtual formulation intervention in polymeric long-acting injectables.Frontiers in medicine · 2026Article
- Artificial Intelligence in Pharmaceutical Drug Development: Transforming Formulation and Innovation.Current drug discovery technologies · 2026Review
- Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review.AAPS PharmSciTech · 2025Review
- Generative Model Enhanced X-ray Computed Tomography Imaging for Pharmaceutical Powder Microstructure Reconstruction and Evaluation.AAPS PharmSciTech · 2025Article
- DeepSeek-LLM with Adaptive RAG for Pharmaceutical Dissolution Prediction.Pharmaceutical research · 2025Article
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- The return of a forgotten ally: tabletop scanning electron microscopy in the realm of bacteriology.Frontiers in cellular and infection microbiology · 2025Review
- Quantitative Structural and Compositional Elucidation of Real-World Pharmaceutical Tablet Using Large Field-of-View, Correlative Microscopy-Tomography Techniques and AI-Enabled Image Analysis.Pharmaceutical research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pharmaceutical drug dosage forms are critical for ensuring the effective and safe delivery of active pharmaceutical ingredients to patients. However, traditional formulation development often relies on extensive lab and animal experimentation, which can be time-consuming and costly. This manuscript presents a generative artificial intelligence method that creates digital versions of drug products from images of exemplar products. This approach employs an image generator guided by critical quality attributes, such as particle size and drug loading, to create realistic digital product variations that can be analyzed and optimized digitally. This paper shows how this method was validated through two case studies: one for the determination of the amount of material that will create a percolating network in an oral tablet product and another for the optimization of drug distribution in a long-acting HIV inhibitor implant. The results demonstrate that the generative AI method accurately predicts a percolation threshold of 4.2% weight of microcrystalline cellulose and generates implant formulations with controlled drug loading and particle size distributions. Comparisons with real samples reveal that the synthesized structures exhibit comparable particle size distributions and transport properties in release media.
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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.