ReviewAdvanced drug delivery reviews2021
Automation and data-driven design of polymer therapeutics.
Review in Advanced drug delivery reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Sequence-defined polymers for predictable gene delivery.Nature reviews. Chemistry · 2026Review
- Self-driving lab for the data-driven design of single-chain polymer nanoparticles.Cell press blue · 2026Article
- Role of High Fidelity Vs. Low Fidelity Experimental Data in Machine Learning Model Performance for Predicting Polymer Solubility.Macromolecular rapid communications · 2026Article
- A user's guide to your first self-driving liquid handling lab.Digital discovery · 2026Article
- Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films.Macromolecules · 2026Article
- Polymer-doxorubicin Conjugates: Redefining Chemotherapy for Breast Cancer.Mini reviews in medicinal chemistry · 2026Article
- The Alchemist, the Scientist, and the Robot: Exploring the Potential of Human-AI Symbiosis in Self-Driving Polymer Laboratories.Macromolecular rapid communications · 2025Review
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- The Role of Artificial Intelligence and Machine Learning in Polymer Characterization: Emerging Trends and Perspectives.Chromatographia · 2025Article
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
- Mapping Biomaterial Complexity by Machine Learning.Tissue engineering. Part A · 2024Review
- Self-Reporting Therapeutic Protein Nanoparticles.ACS applied materials & interfaces · 2024Article
- Recent Review on Biological Barriers and Host-Material Interfaces in Precision Drug Delivery: Advancement in Biomaterial Engineering for Better Treatment Therapies.Pharmaceutics · 2024Review
- Polymer designChemical science · 2024Article
- Automated BigSMILES conversion workflow and dataset for homopolymeric macromolecules.Scientific data · 2024Article
- Efficient ocular delivery of siRNAInternational journal of pharmaceutics: X · 2023Article
- Understanding How Cationic Polymers' Properties Inform Toxic or Immunogenic Responses via Parametric Analysis.Macromolecules · 2023Article
- Artificial intelligence driven design of catalysts and materials for ring opening polymerization using a domain-specific language.Nature communications · 2023Article
- Emerging Trends in Machine Learning: A Polymer Perspective.ACS polymers Au · 2023Review
- Examining polymer-protein biophysical interactions with small-angle x-ray scattering and quartz crystal microbalance with dissipation.Journal of biomedical materials research. Part A · 2023Article
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.
Funding
Abstract
Polymers are uniquely suited for drug delivery and biomaterial applications due to tunable structural parameters such as length, composition, architecture, and valency. To facilitate designs, researchers may explore combinatorial libraries in a high throughput fashion to correlate structure to function. However, traditional polymerization reactions including controlled living radical polymerization (CLRP) and ring-opening polymerization (ROP) require inert reaction conditions and extensive expertise to implement. With the advent of air-tolerance and automation, several polymerization techniques are now compatible with well plates and can be carried out at the benchtop, making high throughput synthesis and high throughput screening (HTS) possible. To avoid HTS pitfalls often described as "fishing expeditions," it is crucial to employ intelligent and big data approaches to maximize experimental efficiency. This is where the disruptive technologies of machine learning (ML) and artificial intelligence (AI) will likely play a role. In fact, ML and AI are already impacting small molecule drug discovery and showing signs of emerging in drug delivery. In this review, we present state-of-the-art research in drug delivery, gene delivery, antimicrobial polymers, and bioactive polymers alongside data-driven developments in drug design and organic synthesis. From this insight, important lessons are revealed for the polymer therapeutics community including the value of a closed loop design-build-test-learn workflow. This is an exciting time as researchers will gain the ability to fully explore the polymer structural landscape and establish quantitative structure-property relationships (QSPRs) with biological significance.
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.