Evidence map›Paper›PMID 40277097›Full record

ReviewCurrent topics in medicinal chemistry2025

Artificial Intelligence-Driven Innovations in Pharmaceutical Development and Drug Delivery Systems.

Ting Zhu, Bing Liu, Ning Chen, Yuchen Liu, Zixuan Wang, Xue Tian

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current topics in medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ting ZhuPharmaceutical Engineering Technology Research Center, Harbin University of Commerce, Harbin, China.
Bing LiuPharmaceutical Engineering Technology Research Center, Harbin University of Commerce, Harbin, China.
Ning ChenPharmaceutical Engineering Technology Research Center, Harbin University of Commerce, Harbin, China.
Yuchen LiuPharmaceutical Engineering Technology Research Center, Harbin University of Commerce, Harbin, China.
Zixuan WangPharmaceutical Engineering Technology Research Center, Harbin University of Commerce, Harbin, China.
Xue TianPharmaceutical Engineering Technology Research Center, Harbin University of Commerce, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As Artificial Intelligence (AI) technology rapidly advances, its application in pharmaceutical formulation design and Drug Delivery Systems (DDS) is expanding, revealing significant potential. AI technology has played a role in optimizing drug design, enhancing research and development efficiency, and improving the safety profiles of pharmaceutical products, thereby supporting the realization of personalized medicine. This article systematically examines the foundational applications and principles of AI in pharmaceutical formulation, while also evaluating its role in key areas such as drug development, manufacturing, quality control, and ADME/T (absorption, distribution, metabolism, excretion, and toxicity) prediction. In particular, AI can enhance prediction accuracy for drug solubility, stability, and bioavailability, while optimizing novel DDS through Machine Learning (ML) models, such as nanocarrier design and personalized drug release control. Furthermore, AI has been pivotal in advancing intelligent manufacturing technologies, including three-dimensional printing (3D printing) and continuous manufacturing. Finally, the article explores the opportunities and challenges presented by AI in drug development, regulation, and policymaking. Overall, AI's integration promises to revolutionize pharmaceutical development and regulatory practices.

Indexed as

Artificial IntelligenceDrug Delivery SystemsDrug DevelopmentHumansMachine LearningPharmaceutical PreparationsPharmaceutical PreparationsArtificial Intelligence (AI)drug delivery systemsMachine Learning (ML)nanomedicinenanoparticlespharmaceutical formulation designpredictive modeling.

Identifiers

PMID40277097

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.