ArticlePharmaceutical research2018
QSAR Development for Plasma Protein Binding: Influence of the Ionization State.
Article in Pharmaceutical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- AI redefine untargeted metabolomics: estimating chemical amounts for a Human Exposome Project.Frontiers in public health · 2026Review
- In Silico ADME Methods Used in the Evaluation of Natural Products.Pharmaceutics · 2025Review
- Comprehensive benchmarking of computational tools for predicting toxicokinetic and physicochemical properties of chemicals.Journal of cheminformatics · 2024Article
- Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans.Archives of toxicology · 2024Article
- Recent Studies of Artificial Intelligence on In Silico Drug Distribution Prediction.International journal of molecular sciences · 2023Review
- Using VEGAHUB Within a Weight-of-Evidence Strategy.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.Environmental science & technology · 2021Article
- Article
- Setting the stage for next-generation risk assessment with non-animal approaches: the EU-ToxRisk project experience.Archives of toxicology · 2020Article
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeThis study explored several strategies to improve the performance of literature QSAR models for plasma protein binding (PPB), such as a suitable endpoint transformation, a correct representation of chemicals, more consistency in the dataset, and a reliable definition of the applicability domain.
methodsWe retrieved human fraction unbound (Fu) data for 670 compounds from the literature and carefully checked them for consistency. Descriptors were calculated taking account of the ionization state of molecules at physiological pH (7.4), in order to better estimate the affinity of molecules to blood proteins. We used different algorithms and chemical descriptors to explore the most suitable strategy for modeling the endpoint. SMILES (simplified molecular input line entry system)-based string descriptors were also tested with the CORAL software (CORelation And Logic). We did an outlier analysis to establish the models to use (or not to use) in case of well recognized families.
resultsInternal validation of the selected models returned Q
conclusionsPerformance in prediction confirmed the robustness of all the derived models and their suitability for real-life purposes, i.e. screening chemicals for their ADMET profiling. Optimization of descriptors can be useful in order to obtain the correct results with a ionized molecule.
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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.