ReviewCurrent drug targets2024
A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology.
Review in Current drug targets, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Biomarkers as Drivers of Innovation in Modern Diagnostics and Therapeutics.Current drug targets · 2026Review
- The Use of Artificial Intelligence in the Formulation of Effervescent Tablets: A Review.Mini reviews in medicinal chemistry · 2026Review
- Exploring the Therapeutic Potential of Plant-Based Natural Products in Combating Aging.Current drug targets · 2026Review
- Watermelon Rind: Nutritional Composition, Therapeutic Potential, Environmental Impact, and Commercial Applications in Sustainable Industries.Current pharmaceutical design · 2026Review
- Emerging Combinatorial Drug Delivery Strategies for Breast Cancer: A Comprehensive Review.Current drug targets · 2025Review
- Lipidomics in Breast Cancer: Decoding Metabolic Reprogramming and Unlocking Therapeutic Opportunities.Current drug targets · 2025Review
- Deciphering the Molecular Mechanisms of Polycystic Ovary Syndrome and Flaxseed Therapy Through Transcriptomics and Machine Learning.IET systems biologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science.
objectivesThe primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes.
methodsThis review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques.
resultsRecent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process.
conclusionThe integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.
Indexed as
Identifiers
38213164What 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.