ArticleFrontiers in chemistry2018
Prediction Is a Balancing Act: Importance of Sampling Methods to Balance Sensitivity and Specificity of Predictive Models Based on Imbalanced Chemical Data Sets.
Article in Frontiers in chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 1 of them a synthesis that pooled it.
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Who cites it
75 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach.Journal of computer-aided molecular design · 2026Article
- Predicting peroxisome proliferator-activated receptor gamma potency of small molecules: a synergistic consensus model and deep learning binding affinity approach powered by Enalos Cloud Platform.Molecular diversity · 2026Article
- Prediction of drug-induced nephrotoxicity based on deep learning algorithm and molecular fingerprints.Molecular diversity · 2026Article
- Qualitative and quantitative in silico toxicity profiling of "angel dust": phencyclidine (PCP) analogues as new psychoactive substances (3-HO-PCP, 3-MeO-PCP, 4-MeO-PCP, 3-HO-PCE, 3-MeO-PCE, 4-MeO-PCE).Archives of toxicology · 2026Article
- Predicting the likelihood of hypoadrenocorticism in dogs using signalment and routine laboratory results with an ensemble machine learning predictive model.Journal of veterinary internal medicine · 2026Article
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- Adjusted imbalance ratio leads to effective AI-based drug discovery against infectious disease.Scientific reports · 2025Article
- Computational screening of umami tastants using deep learning.Molecular diversity · 2025Article
- Synthesis and Biological Evaluation of Bicyclic Pyrazolines with Promising Antimicrobial Activities.ChemMedChem · 2025Article
- AllergyPred: a web server for allergen prediction.Nucleic acids research · 2025Article
- Investigation of Thiazolidine-2,4-Dione Derivatives as Acetylcholinesterase Inhibitors: Synthesis, In Vitro Biological Activities and In Silico Studies.ChemistryOpen · 2025Article
- Antibacterial and photocatalytic potential of piperine-derived zinc oxide nanoparticles against multi-drug-resistant non-typhoidal Salmonella spp.BMC microbiology · 2025Article
- Chloride and Acetonitrile Ruthenium(IV) Complexes: Crystal Architecture, Chemical Characterization, Antibiofilm Activity, and Bioavailability in Biological Systems.Molecules (Basel, Switzerland) · 2025Article
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- Prediction of aflatoxin contamination outbreaks in Texas corn using mechanistic and machine learning models.Frontiers in microbiology · 2025Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increase in the number of new chemicals synthesized in past decades has resulted in constant growth in the development and application of computational models for prediction of activity as well as safety profiles of the chemicals. Most of the time, such computational models and its application must deal with imbalanced chemical data. It is indeed a challenge to construct a classifier using imbalanced data set. In this study, we analyzed and validated the importance of different sampling methods over non-sampling method, to achieve a well-balanced sensitivity and specificity of a machine learning model trained on imbalanced chemical data. Additionally, this study has achieved an accuracy of 93.00%, an AUC of 0.94, F1 measure of 0.90, sensitivity of 96.00% and specificity of 91.00% using SMOTE sampling and Random Forest classifier for the prediction of Drug Induced Liver Injury (DILI). Our results suggest that, irrespective of data set used, sampling methods can have major influence on reducing the gap between sensitivity and specificity of a model. This study demonstrates the efficacy of different sampling methods for class imbalanced problem using binary chemical data sets.
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