ArticleMolecules (Basel, Switzerland)2023
Prediction of ADMET Properties of Anti-Breast Cancer Compounds Using Three Machine Learning Algorithms.
Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 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
- Multi-Endpoint Prediction of Bioactivity and ADMET Properties Coupled with Multi-Objective Optimization in Molecular Descriptor Space for Candidate ERα Antagonists.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Enhancing ERα-targeted compound efficacy in breast cancer threapy with ExplainableAI and GeneticAlgorithm.PloS one · 2025Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, machine learning methods have been applied successfully in many fields. In this paper, three machine learning algorithms, including partial least squares-discriminant analysis (PLS-DA), adaptive boosting (AdaBoost), and light gradient boosting machine (LGBM), were applied to establish models for predicting the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET for short) properties, namely Caco-2, CYP3A4, hERG, HOB, MN of anti-breast cancer compounds. To the best of our knowledge, the LGBM algorithm was applied to classify the ADMET property of anti-breast cancer compounds for the first time. We evaluated the established models in the prediction set using accuracy, precision, recall, and F1-score. Compared with the performance of the models established using the three algorithms, the LGBM yielded most satisfactory results (accuracy > 0.87, precision > 0.72, recall > 0.73, and F1-score > 0.73). According to the obtained results, it can be inferred that LGBM can establish reliable models to predict the molecular ADMET properties and provide a useful tool for virtual screening and drug design researchers.
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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.