ArticlePharmaceuticals (Basel, Switzerland)2024
A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru.
Article in Pharmaceuticals (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Breaking through the radiation dilemma: development and clinical translation of anti-radiation drugs.Pharmaceutical science advances · 2026Review
- Molecular descriptor driven QSPR modeling of Papp, TEER and Efflux Ratio from Caco-2 cells using machine learning for various phytochemicals.Journal of the science of food and agriculture · 2026Article
- CaliciBoost: Performance-driven evaluation of molecular representations for caco-2 permeability prediction.Journal of cheminformatics · 2025Article
- Developing a quantitative structure-property relationships (QSPR) model using Caco-2 cell bioavailability indicators (BA) to predict the BA of phytochemicals.Journal of the science of food and agriculture · 2025Article
- Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation.Scientific reports · 2025Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundPeru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure-property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru.
methodsA dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM-RF-GBM model.
resultsAn evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R
conclusionsOur results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts.
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