ReviewAAPS PharmSciTech2020
Digital Pharmaceutical Sciences.
Review in AAPS PharmSciTech, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed.
- Precision Drug Delivery of LY-11h for Acute Myeloid Leukemia Treatment Using Machine Learning-Assisted Hot Melt Extrusion and 3D-Printed Technologies.Pharmaceutics · 2026Article
- Towards deployable CRISPR-based nucleic acid detection.Progress in biomedical engineering (Bristol, England) · 2026Review
- Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.Acta pharmaceutica Sinica. B · 2026Review
- Advancing Pharmaceutical Science with Artificial Neural Networks: A Review on Optimizing Drug Delivery Systems Formulation.Current pharmaceutical design · 2025Review
- Current and future prospective of pharmaceutical manufacturing in Saudi Arabia.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2023Article
- Novel 3D Printed Modular Tablets Containing Multiple Anti-Viral Drugs: a Case of High Precision Drop-on-Demand Drug Deposition.Pharmaceutical research · 2022Article
- Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.Molecular diversity · 2022Review
- Editorial: The Dual-Use Dilemma for Biomimicry.Frontiers in molecular biosciences · 2022Article
- IntegratedActa pharmaceutica Sinica. B · 2021Article
- Application of Machine-Learning Algorithms for Better Understanding of Tableting Properties of Lactose Co-Processed with Lipid Excipients.Pharmaceutics · 2021Article
- Microfluidic Synthesis of Indomethacin-Loaded PLGA Microparticles Optimized by Machine Learning.Frontiers in molecular biosciences · 2021Article
- Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations-A Particle Technology Perspective.Pharmaceutics · 2020Review
- Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics.Scientific reports · 2020Article
- Application of Dominance-Based Rough Set Approach for Optimization of Pellets Tableting Process.Pharmaceutics · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-to-day pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.
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.