ArticleJournal of cheminformatics2025
PKSmart: an open-source computational model to predict intravenous pharmacokinetics of small molecules.
Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Integration of in vitro and in silico approaches enables prediction of drug-induced liver injury.Archives of toxicology · 2026Article
- Pharmacokinetic Prediction of Repurposed Drugs for PDAC Using Artificial Intelligence.ACS omega · 2026Article
- Integrating Pharmacokinetics and Quantitative Systems Pharmacology Approaches in Generative Drug Design.Journal of chemical information and modeling · 2025Article
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Application of Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans.Journal of medicinal chemistry · 2025Article
- Predicting Pharmacokinetics in Rats Using Machine Learning: A Comparative Study Between Empirical, Compartmental, and PBPK-Based Approaches.Clinical and translational science · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Improved Detection of Drug-Induced Liver Injury by Integrating PredictedChemical research in toxicology · 2024Article
- Step Forward Cross Validation for Bioactivity Prediction: Out of Distribution Validation in Drug Discovery.bioRxiv : the preprint server for biology · 2024Article
- Improved Detection of Drug-Induced Liver Injury by Integrating PredictedbioRxiv : the preprint server for biology · 2024Article
- A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.bioRxiv : the preprint server for biology · 2024Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Drug exposure, a key determinant of drug safety and efficacy, is governed by pharmacokinetic (PK) parameters such as volume of distribution (VDss), clearance (CL), half-life (t½), fraction unbound in plasma (fu), and mean residence time (MRT). In this study, we developed machine learning models to predict human PK parameters for 1,283 unique compounds using molecular structure, physicochemical properties, and predicted animal PK data. Our approach involved a two-stage modeling pipeline. First, we trained models to predict rat, dog, and monkey PK parameters (VDss, CL, fu) from chemical structure and properties for 371 compounds. These models were used to predict animal PK values for 1,283 unique compounds with human PK data. These animal PK predictions were then integrated with molecular descriptors and fingerprints to build Random Forest models for human PK parameters. The models demonstrated consistent performance across nested cross-validation and external validation sets, with predictive accuracy for VDss comparable to proprietary models developed by AstraZeneca. Notably, human VDss and CL predictions achieved external R
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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.