ArticleJournal of chemical information and modeling2020
Consensus versus Individual QSARs in Classification: Comparison on a Large-Scale Case Study.
Article in Journal of chemical information and modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Machine learning-based prediction of cross-immunity.Briefings in bioinformatics · 2026Article
- Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials.Environmental science & technology · 2026Review
- Leveraging Consensus Docking Approaches for Human Mitochondrial Complexes I and III.Chemical research in toxicology · 2026Article
- Machine Learning-Driven Consensus Modeling for Activity Ranking and Chemical Landscape Analysis of HIV-1 Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Article
- An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction.International journal of molecular sciences · 2024Article
- "DompeKeys": a set of novel substructure-based descriptors for efficient chemical space mapping, development and structural interpretation of machine learning models, and indexing of large databases.Journal of cheminformatics · 2024Article
- The pursuit of accurate predictive models of the bioactivity of small molecules.Chemical science · 2024Review
- Development and application of consensusFrontiers in pharmacology · 2024Article
- The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential.Beilstein journal of nanotechnology · 2024Article
- Structural Characterization and Cytotoxic Activity Evaluation of Ulvan Polysaccharides Extracted from the Green AlgaeMarine drugs · 2023Article
- Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features.BMC bioinformatics · 2023Article
- Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets.Chemical research in toxicology · 2023Article
- Leveraging molecular structure and bioactivity with chemical language models for de novo drug design.Nature communications · 2023Article
- Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood-Brain Barrier Permeability.International journal of molecular sciences · 2022Article
- Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data.Molecules (Basel, Switzerland) · 2022Article
- Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency.International journal of environmental research and public health · 2022Article
- Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies.Regulatory toxicology and pharmacology : RTP · 2021Article
- In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.Molecular diversity · 2021Article
Corrections and comments
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Consensus strategies have been widely applied in many different scientific fields, based on the assumption that the fusion of several sources of information increases the outcome reliability. Despite the widespread application of consensus approaches, their advantages in quantitative structure-activity relationship (QSAR) modeling have not been thoroughly evaluated, mainly due to the lack of appropriate large-scale data sets. In this study, we evaluated the advantages and drawbacks of consensus approaches compared to single classification QSAR models. To this end, we used a data set of three properties (androgen receptor binding, agonism, and antagonism) for approximately 4000 molecules with predictions performed by more than 20 QSAR models, made available in a large-scale collaborative project. The individual QSAR models were compared with two consensus approaches, majority voting and the Bayes consensus with discrete probability distributions, in both protective and nonprotective forms. Consensus strategies proved to be more accurate and to better cover the analyzed chemical space than individual QSARs on average, thus motivating their widespread application for property prediction. Scripts and data to reproduce the results of this study are available for download.
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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.