ArticleJournal of cheminformatics2020
Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications.
Article in Journal of cheminformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- An Explainable Machine Learning-Based QSAR Framework for Predicting Thrombin Inhibitory Activity.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest.Materials (Basel, Switzerland) · 2026Article
- Deep learning for assay nuisance compound detection using a gated co-attention graph embedding model (CAGE-Fusion).Journal of cheminformatics · 2026Article
- Machine learning-based QSPR modeling for predicting theiScience · 2026Article
- A Machine Learning Approach for Predicting the Pure-Component Surface Tension of Atmospherically Relevant Organic Compounds.ACS ES&T air · 2025Article
- Predicting the academic achievement of students using black hole optimization and Gaussian process regression.Scientific reports · 2025Article
- Enhancing fever of unknown origin diagnosis: machine learning approaches to predict metagenomic next-generation sequencing positivity.Frontiers in cellular and infection microbiology · 2025Article
- Research on the optimization model of anti-breast cancer candidate drugs based on machine learning.Frontiers in genetics · 2025Article
- Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea.Scientific reports · 2024Article
- Application of hybridized ensemble learning and equilibrium optimization in estimating damping ratios of municipal solid waste.Scientific reports · 2024Article
- Research on predicting the driving forces of digital transformation in Chinese media companies based on machine learning.Scientific reports · 2024Article
- Article
- De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3β Inhibitors Targeting Neurofibrillary Tangles.International journal of molecular sciences · 2024Article
- Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis.Research (Washington, D.C.) · 2024Article
- Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction.The journal of physical chemistry. B · 2023Article
- TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining.Chemical science · 2023Article
- Discovering the Active Ingredients of Medicine and Food Homologous Substances for Inhibiting the Cyclooxygenase-2 Metabolic Pathway by Machine Learning Algorithms.Molecules (Basel, Switzerland) · 2023Article
- Immediate word recall in cognitive assessment can predict dementia using machine learning techniques.Alzheimer's research & therapy · 2023Article
- Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.Molecules (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR). With the growing number of ensemble learning models such as random forest, the effectiveness of QSAR/QSPR will be limited by the machine's inability to interpret the predictions to researchers. In fact, many implementations of ensemble learning models are able to quantify the overall magnitude of each feature. For example, feature importance allows us to assess the relative importance of features and to interpret the predictions. However, different ensemble learning methods or implementations may lead to different feature selections for interpretation. In this paper, we compared the predictability and interpretability of four typical well-established ensemble learning models (Random forest, extreme randomized trees, adaptive boosting and gradient boosting) for regression and binary classification modeling tasks. Then, the blending methods were built by summarizing four different ensemble learning methods. The blending method led to better performance and a unification interpretation by summarizing individual predictions from different learning models. The important features of two case studies which gave us some valuable information to compound properties were discussed in detail in this report. QSPR modeling with interpretable machine learning techniques can move the chemical design forward to work more efficiently, confirm hypothesis and establish knowledge for better results.
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