ReviewAAPS PharmSciTech2024
The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.
Review in AAPS PharmSciTech, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- Process Analytical Technology Integration in 3D Printing Processes: Benefits and Challenges.AAPS PharmSciTech · 2026Review
- Artificial Intelligence-Driven Nanomedicine: From Drug Formulation and Nanocarrier Design to Clinical Translation.Pharmaceutics · 2026Review
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 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
- Specification Limit Normalization for Nonparametric Process Capability Analysis and Its Application in Cleaning Validation with AI-Enabled Monitoring Models.AAPS PharmSciTech · 2026Article
- Artificial neural network as a strategy to predict rheological properties in emulgel formulations.Scientific reports · 2026Article
- Implementing QbD for Nano-Pharmaceuticals and Complex Formulations to Achieve Predictable and High-Quality Outcomes.AAPS PharmSciTech · 2026Review
- AI-driven pilot platforms and computational pharmaceutics: accelerating innovation in small molecule drug development under industry 4.0 and 5.0 paradigms.Frontiers in pharmacology · 2026Review
- Advances in Nose-to-Brain Delivery Systems for Effective Alzheimer's Disease Management.Current Alzheimer research · 2026Review
- Analysis of the interaction network relationship between drugs using a graph neural network.Frontiers in pharmacology · 2026Article
- Intelligent information management enables quality-by-design in pharmaceutical production.Scientific reports · 2025Article
- Cytotoxic and Antimicrobial Activity of theMolecules (Basel, Switzerland) · 2025Review
- MakingInternational journal of pharmaceutics: X · 2025Article
- Review
- Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review.AAPS PharmSciTech · 2025Review
- Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation.Journal of chemical information and modeling · 2025Article
- Multistep Machine Learning Pipeline For Polymeric Nanoparticle Design.AAPS PharmSciTech · 2025Article
- Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.European journal of medical research · 2025Review
- The Use and Importance ofArtificial Intelligence in Vaccine Research, Development, and Production.Archives of Razi Institute · 2025Review
- The Pharmaceutical Industry's Future: How Artificial Intelligence is Transforming Medicine.Advanced pharmaceutical bulletin · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.
Indexed as
Identifiers
39147952What 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.