ArticleJournal of chemical information and modeling2025
Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Cracking ERα Y537S Resistance: Explainable Machine Learning-Guided Discovery and Molecular Dynamics Validation of Stable Candidate Ligands.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Multi-database pharmacovigilance identifies disproportionate reporting of hepatobiliary events with avacopan: an integrative study with network pharmacology and interpretable machine learning.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- A Combined Chemoinformatics- and Machine Learning-Based Approach Identifies Chlormidazole as a Drug Repurposing Candidate against Aggressive Prostate Cancer.Journal of medicinal chemistry · 2026Article
- Near-Infrared Spectroscopy Coupled with Chemometrics for Rapid Determination of pH, Moisture and Lycopene Content in Lycopene Liquid Beverages.Foods (Basel, Switzerland) · 2026Article
- Classification of different light colors applied during the incubation period based on small intestine morphology with XGBoost algorithm.BMC veterinary research · 2026Article
- Machine Learning-Driven QSRR Modeling of Albumin Binding in Fluoroquinolones: An SVR Approach Supported by HSA Chromatography.International journal of molecular sciences · 2026Article
- Predicting blood-brain barrier permeability of chemicals by machine learning modeling.NAM journal · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tree-based machine learning (ML) algorithms, such as Extra Trees (ET), Random Forest (RF), Gradient Boosting Machine (GBM), and XGBoost (XGB) are among the most widely used in early drug discovery, given their versatility and performance. However, models based on these algorithms often suffer from misclassification and reduced interpretability issues, which limit their applicability in practice. To address these challenges, several approaches have been proposed, including the use of SHapley Additive Explanations (SHAP). While SHAP values are commonly used to elucidate the importance of features driving models' predictions, they can also be employed in strategies to improve their prediction performance. Building on these premises, we propose a novel approach that integrates SHAP and features value analyses to reduce misclassification in model predictions. Specifically, we benchmarked classifiers based on ET, RF, GBM, and XGB algorithms using data sets of compounds with known antiproliferative activity against three prostate cancer (PC) cell lines (
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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.