ArticleNature communications2023
Machine learning models to accelerate the design of polymeric long-acting injectables.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 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
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Who cites it
94 citing papers in PubMed, 190 citations in OpenAlex.
- Active learning in pharmaceutical 3D printing: a multi-dataset comparison.Drug delivery and translational research · 2026Article
- Engineering Oral Nanoparticles: Navigating Biological Barriers in the Gastrointestinal Tract.Pharmaceutical research · 2026Review
- On-demand design of controlled-release systems using an expert-mimic AI framework.Nature communications · 2026Article
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- Nanoparticulate and Hydrogel Vehicles for Stimuli-Responsive and Sustained Controlled Release of Active Pharmaceutical Ingredients.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A Machine Learning Platform for Imputing Missing Critical Quality Attributes in Pharmaceutical Development.The AAPS journal · 2026Article
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- MC-NODE: A Mechanism-Decomposed Neural Differential Model for PLGA Microsphere Drug Release Prediction and Attribution.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A novel machine learning framework-based rapid screening of ionizable lipids in LNPs for highly-efficient mRNA expression.Acta pharmaceutica Sinica. B · 2026Article
- Exploration of Development Parameters for IgG Microspheres Using an Automated Platform for High-Throughput Formulation Screening.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Predicting drug release from polymeric long-acting injectables using a machine learning approach to decode formulation-performance relationships.Drug delivery and translational research · 2026Article
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Diagnosing the Information Limits of In Vitro Drug Release from PLGA Microparticle Data.Pharmaceutics · 2026Article
- Predicting early and complete drug release from long-acting injectables using explainable machine learning.International journal of pharmaceutics · 2026Article
- Artificial intelligence-assisted design and optimization of stimuli-responsive nanocarriers for smart drug delivery.Materials today. Bio · 2026Review
- Interpretable Two-Stage Machine Learning for Early and Full Drug Release Prediction in PLGA Microspheres.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Electrosprayed PLGA Nanoparticles for Dual Drug Delivery: Design, Optimization and Applications.Polymers · 2026Review
- Predictive Neural Network Modeling of Nanoporous Anodic Alumina for Controlled Drug Release Implants: An Integrated Machine Learning Approach.Materials (Basel, Switzerland) · 2026Article
- Review
- Review
34 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development.
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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.