Hybrid Class Balancing Approach for Chemical Compound Toxicity Prediction

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:29:49Z
Fecha de publicación2025-01-01
ResumenIntroduction: Computational methods are crucial for efficient and cost-effective drug toxicity prediction. Unfortunately, the data used for prediction is often imbalanced, resulting in biased models that favor the majority class. This paper proposes an approach to apply a hybrid class balancing technique and evaluate its performance on computational models for toxicity prediction in Tox21 datasets. Methods: The process begins by converting chemical compound data structures (SMILES strings) from various bioassay datasets into molecular descriptors that can be processed by algorithms. Subsequently, Undersampling and Oversampling techniques are applied in two different schemes on the training data. In the first scheme (Individual), only one balancing technique (Oversampling or Undersampling) is used. In the second scheme (Hybrid), the training data is divided according to a ratio (e.g., 90-10), applying a different balancing technique to each proportion. We considered eight resampling techniques (four Oversampling and four Undersampling), six molecular descriptors (based on MACCS, ECFP, and Mordred), and five classification models (KNN, MLP, RF, XGB and SVM) over 10 bioassay datasets to determine the configurations that yield the best performance. Results: We defined three testing scenarios: without balancing techniques (baseline), Individual, and Hybrid. We found that using the ENN technique in the MACCS-MLP combination resulted in a 10.01% improvement in performance. The increase for ECFP6-2048 was 16.47% after incorporating a combination of the SMOTE (10%) and RUS (90%) techniques. Meanwhile, using the same combination of techniques, MORDRED-XGB showed the most significant increase in performance, achieving a 22.62% improvement. Conclusion: Integrating any of the class balancing schemes resulted in a minimum of 10.01% improvement in prediction performance compared to the best baseline configuration. In this study, Undersampling techniques were more appropriate due to the significant overlap among samples. By eliminating specific samples from the predominant class that are close to the minority class, this overlap is greatly reduced.es_ES
Doihttps://doi.org/10.2174/0115734099315538240909101737es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1766
Idiomaeses_ES
EditorialBentham Science Publishers Ltd.es_ES
RelaciónCurrent Computer-Aided Drug Designes_ES
URL relacionadohttps://doi.org/10.2174/0115734099315538240909101737es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteCurrent Computer-Aided Drug Design
TítuloHybrid Class Balancing Approach for Chemical Compound Toxicity Predictiones_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorSantiago-Gonzalez, Felipe
AutorMartinez-Rodriguez, Jose L.
AutorGarcía-Perez, Carlos
AutorJuarez-Saldivar, Alfredo
AutorCamacho-Cruz, Hugo E.
AutorSantiago-Gonzalez, Felipees_ES
AutorMartinez-Rodriguez, Jose L.es_ES
AutorGarcía-Perez, Carloses_ES
AutorJuarez-Saldivar, Alfredoes_ES
AutorCamacho-Cruz, Hugo E.es_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número8es_ES
Rango de páginas1093-1107es_ES
URL relacionadahttps://doi.org/10.2174/0115734099315538240909101737
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen21es_ES

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