ArticleActa pharmaceutica Sinica. B2019
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Article in Acta pharmaceutica Sinica. B, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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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
48 citing papers in PubMed.
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- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Application of AI in Tablet Development: An Integrated Machine Learning Framework for Pre-Formulation Property Prediction.Pharmaceutics · 2026Article
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Leakage-aware machine learning for data-driven performance prediction of metal-organic framework systems.Frontiers in medicine · 2026Article
- Combined Experimental and Computational Approaches for Ternary Solid Dispersions to Enhance the Oral Bioavailability of Penfluridol.Pharmaceutics · 2025Article
- Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review.AAPS PharmSciTech · 2025Review
- Evaluating the Vascular Risk of PFCs: An Integrated XGBoost-Driven Structure-Activity Prediction and Experimental Validation Study.Environment & health (Washington, D.C.) · 2025Article
- Artificial intelligence and anti-cancer drugs' response.Acta pharmaceutica Sinica. B · 2025Review
- Advancing drug delivery research: sustainable strategies for innovation and translation.Drug delivery and translational research · 2025Review
- Applications of Artificial Intelligence in Drug Repurposing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.Pharmaceutical research · 2025Article
- The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.AAPS PharmSciTech · 2024Review
- Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models.Pharmaceutics · 2024Review
- Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface.AAPS PharmSciTech · 2024Article
- A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests.BMC medical informatics and decision making · 2024Article
- In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin.Functional composite materials · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.
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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.