ArticleADMET & DMPK2020
ADME prediction with KNIME:
Article in ADMET & DMPK, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Smart Formulation: AI-Driven Web Platform for Optimization and Stability Prediction of Compounded Pharmaceuticals Using KNIME.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Article
- Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models.Pharmaceutics · 2024Review
- Will we ever be able to accurately predict solubility?Scientific data · 2024Article
- A new workflow for the effective curation of membrane permeability data from open ADME information.Journal of cheminformatics · 2024Article
- Exploring the Inhibitory Potential ofTheScientificWorldJournal · 2024Article
- Antifungal Constituents ofMolecules (Basel, Switzerland) · 2023Article
- The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery.Pharmaceutics · 2023Review
- In silico Study of Antiviral Activity of Polyphenol Compounds fromAdvances and applications in bioinformatics and chemistry : AABC · 2023Article
- Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood-Brain Barrier Permeability.International journal of molecular sciences · 2022Article
- Intrinsic Aqueous Solubility: Mechanistically Transparent Data-Driven Modeling of Drug Substances.Pharmaceutics · 2022Article
- Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches.Pharmaceutics · 2022Article
- Article
- Article
- ADME prediction with KNIME: A retrospective contribution to the second "Solubility Challenge".ADMET & DMPK · 2021Article
- Strategies of solubility enhancement and perspectives in solubility measurements of pharmaceutical compounds.ADMET & DMPK · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes. For many years, the limited performance of in-silico solubility models has been attributed to the lack of high-quality solubility data for pharmaceutical molecules. However, some studies suggest that the poor accuracy of solubility prediction is not related to the quality of the experimental data and that more precise methodologies (algorithms and/or set of descriptors) are required for predicting aqueous solubility for pharmaceutical molecules. In this study a large and diverse database was generated with aqueous solubility values collected from two public sources; two new recursive machine-learning approaches were developed for data cleaning and variable selection, and a consensus model based on regression and classification algorithms was created. The modeling protocol, which includes the curation of chemical and experimental data, was implemented in KNIME, with the aim of obtaining an automated workflow for the prediction of new databases. Finally, we compared several methods or models available in the literature with our consensus model, showing results comparable or even outperforming previous published models.
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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.