ArticleJournal of chemical information and modeling2022
Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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
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.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review.European journal of drug metabolism and pharmacokinetics · 2024Pooled it
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- Application of New Approach Methodologies to Improve Oral Biopharmaceutic Assessments.Pharmaceutics · 2026Review
- Pharmacokinetic Prediction of Repurposed Drugs for PDAC Using Artificial Intelligence.ACS omega · 2026Article
- Advancing the development of deep learning and machine learning models for oral drugs through diverse descriptor classes: a focus on pharmacokinetic parameters (Vdss and PPB).Molecular diversity · 2026Article
- Biological and Teratogenic Evaluations of Nitrogen Heterocycles for Anticancer Therapy.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- Next-Generation Artificial Intelligence for ADME Prediction in Drug Discovery: From Small Molecules to Biologics.Yonago acta medica · 2026Review
- Machine learning-based unified models for predicting drug clearance from pharmacokinetic animal and study design variables.PloS one · 2026Article
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- Enhancing molecular property prediction through data integration and consistency assessment.Journal of cheminformatics · 2025Article
- Computational approaches for toxicology and Pharmacokinetic properties prediction.Journal of pharmacokinetics and pharmacodynamics · 2025Review
- Fraction-based Linear Extrapolation (FLEX) Method for Predicting Human Pharmacokinetic Clearance: Advanced Allometric Scaling Method and Machine Learning Approach.Pharmaceutical research · 2025Article
- Integrating Pharmacokinetics and Quantitative Systems Pharmacology Approaches in Generative Drug Design.Journal of chemical information and modeling · 2025Article
- Application of Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans.Journal of medicinal chemistry · 2025Article
- Refined ADME Profiles for ATC Drug Classes.Pharmaceutics · 2025Article
- Neuroprotective Potential ofBiomolecules · 2025Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CL
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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.