ArticleJournal of cheminformatics2014
The influence of negative training set size on machine learning-based virtual screening.
Article in Journal of cheminformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Complex Networks in Bioactive Peptide Research: A Methodological Review.Biomolecules · 2026Review
- Antibiotic Resistance Crisis: From Bacterial Bioprospecting to Artificial Intelligence.Environmental microbiology reports · 2025Review
- Efficient decoy selection to improve virtual screening using machine learning models.Journal of cheminformatics · 2025Article
- Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions.NPJ science of food · 2025Article
- Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data.Computational and structural biotechnology journal · 2023Review
- Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning.International journal of environmental research and public health · 2022Article
- wSDTNBI: a novel network-based inference method for virtual screening.Chemical science · 2022Article
- Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty.Journal of cheminformatics · 2021Article
- The role of artificial intelligence in the battle against antimicrobial-resistant bacteria.Current genetics · 2021Review
- Property-Unmatched Decoys in Docking Benchmarks.Journal of chemical information and modeling · 2021Article
- Artificial Intelligence in Functional Food Ingredient Discovery and Characterisation: A Focus on Bioactive Plant and Food Peptides.Frontiers in genetics · 2021Review
- Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review.JAMIA open · 2020Review
- Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.Briefings in bioinformatics · 2019Review
- Computational Drug Repurposing Algorithm Targeting TRPA1 Calcium Channel as a Potential Therapeutic Solution for Multiple Sclerosis.Pharmaceutics · 2019Article
- Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking.Molecules (Basel, Switzerland) · 2019Article
- How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors.Frontiers in chemistry · 2018Article
- Article
- A novel approach for the prediction of species-specific biotransformation of xenobiotic/drug molecules by the human gut microbiota.Scientific reports · 2017Article
- Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.Molecular diversity · 2017Article
- Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds.Journal of chemical information and modeling · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods.
resultsThe impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set.
conclusionsIn conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.
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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.