ArticleThe AAPS journal2025
A Machine Learning-Empowered Quantitative Structure-Activity Relationship Model for Predicting the Plasma Half-life of Drugs in Dogs.
Article in The AAPS journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding a drug's plasma half-life is essential in guiding dosage regimens and optimizing therapeutic outcomes, particularly in the early stages of drug development. By using published pharmacokinetic data from Food Animal Residue Avoidance Databank, we collected 560 data points of plasma half-lives for different drugs in dogs following intravenous administration. The dataset was then preprocessed and the mean elimination half-life for each drug was selected in the final clean dataset for model training and testing. Five types of chemical descriptors and four types of supervised machine learning (ML) algorithms were employed to build ML-empowered Quantitative Structure-Activity Relationship (QSAR) models. Model performances were assessed by determination coefficient (R
Indexed as
Identifiers
41254440What 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.