ReviewPharmaceutics2022
State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation.
Review in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Zonisamide nanodiamonds for brain targeting: A comprehensive study utilising in silico, in vitro, in vivo, and molecular investigation for successful nose-to-brain delivery for epilepsy management.Drug delivery and translational research · 2026Article
- Aerosolized Quercetin-Loaded Chia Seed Polysaccharide Nanoparticles: Design of Experiments and Machine-Learning-Guided Optimization for Enhanced Lung Cancer Cell Delivery.Pharmaceutics · 2026Article
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Pulmonary Drug Delivery in the Era of Nanomedicine: From Biological Barriers to Artificial Intelligence-Driven Optimization.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Green manufacturing of amoxicillin trihydrate: a malic acid-assisted crystallization framework enhanced by Taguchi-ANN optimization.RSC advances · 2026Article
- Research Progress in Artificial Intelligence-Assisted Preparation of High-Quality Biomaterials.ACS omega · 2026Review
- Recent Trends in the Development and Clinical Translation of Polymer-based Targeted Therapeutic Nanoparticle.AAPS PharmSciTech · 2026Review
- Advanced microfluidic techniques for the preparation of solid lipid nanoparticles: Innovations and biomedical applications.International journal of pharmaceutics: X · 2025Review
- Artificial intelligence in human immunodeficiency virus mutation prediction and drug design: Advancing personalized treatment and prevention.Pharmaceutical science advances · 2025Review
- NCIVISION: A Siamese Neural Network for Molecular Similarity Prediction MEP and RDG Images.Molecules (Basel, Switzerland) · 2025Article
- Duality of Simplicity and Accuracy in QSPR: A Machine Learning Framework for Predicting Solubility of Selected Pharmaceutical Acids in Deep Eutectic Solvents.Molecules (Basel, Switzerland) · 2025Article
- Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations.Pharmaceutics · 2025Review
- BRAFPred: A Novel Approach for Accurate Prediction of the B-Type Rapidly Accelerated Fibrosarcoma Inhibitor.ACS omega · 2025Article
- Optimizing Neuroprotective Nano-structured Lipid Carriers for Transdermal Delivery through Artificial Neural Network.Pharmaceutical nanotechnology · 2025Article
- Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside.Cureus · 2023Review
- Article
- Pharma 4.0-Artificially Intelligent Digital Twins for Solidified Nanosuspensions.Pharmaceutics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.
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.