ArticleBMC bioinformatics2019
Comprehensive ensemble in QSAR prediction for drug discovery.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed.
- A Machine Learning Approach for Predicting Synergism of Mixtures of Compounds in Treating Cancers.International journal of molecular sciences · 2026Article
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- The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.Biology · 2026Review
- How quantum-chemical geometry optimization level affects classical 3D descriptors and QSAR performance: a comparative study.Journal of cheminformatics · 2026Article
- Machine Learning Prediction of Protein Adsorption on Drug-delivering Nanoparticles: A Literature Survey and Need for Future Development.Pharmaceutical research · 2026Review
- Review
- Cheminformatics and Machine Learning-Driven QSAR Analysis of SPHK2 Inhibitors for Anticancer Drug Design.Current medicinal chemistry · 2026Article
- A case study of the application of AI to early stage drug discovery.Scientific reports · 2025Article
- Multimodal Cross-Attention Molecular Property Prediction for Text, Sequence, Graph, and Geometry.ACS omega · 2025Article
- Alternative methods for developmental toxicity testing: features, status and future directions.Journal of veterinary science · 2025Review
- Identification of novel DYRK1A inhibitors as treatment options for alzheimer's disease through comprehensive in silico approaches.Scientific reports · 2025Article
- Advancing drug development with "Fit-for-Purpose" modeling informed approaches.Journal of pharmacokinetics and pharmacodynamics · 2025Review
- DDI-CYP: Metabolism ensemble models for drug-drug interaction predictions.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- FusionCLM: enhanced molecular property prediction via knowledge fusion of chemical language models.Journal of cheminformatics · 2025Article
- Unveiling the Endocrine-Disrupting Potential of Plant-Derived Compounds: An Ecotoxicological Review.Toxins · 2025Review
- Informatics-Based Design of Virtual Libraries of Polymer Nano-Composites.International journal of molecular sciences · 2025Article
- Digital Alchemy: The Rise of Machine and Deep Learning in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- Predictive Modeling of Pesticides Reproductive Toxicity in Earthworms Using Interpretable Machine-Learning Techniques on Imbalanced Data.ACS omega · 2025Article
- Enhancing ERα-targeted compound efficacy in breast cancer threapy with ExplainableAI and GeneticAlgorithm.PloS one · 2025Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundQuantitative structure-activity relationship (QSAR) is a computational modeling method for revealing relationships between structural properties of chemical compounds and biological activities. QSAR modeling is essential for drug discovery, but it has many constraints. Ensemble-based machine learning approaches have been used to overcome constraints and obtain reliable predictions. Ensemble learning builds a set of diversified models and combines them. However, the most prevalent approach random forest and other ensemble approaches in QSAR prediction limit their model diversity to a single subject.
resultsThe proposed ensemble method consistently outperformed thirteen individual models on 19 bioassay datasets and demonstrated superiority over other ensemble approaches that are limited to a single subject. The comprehensive ensemble method is publicly available at http://data.snu.ac.kr/QSAR/ .
conclusionsWe propose a comprehensive ensemble method that builds multi-subject diversified models and combines them through second-level meta-learning. In addition, we propose an end-to-end neural network-based individual classifier that can automatically extract sequential features from a simplified molecular-input line-entry system (SMILES). The proposed individual models did not show impressive results as a single model, but it was considered the most important predictor when combined, according to the interpretation of the meta-learning.
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