Building of Robust and Interpretable QSAR Classification Models by Means of the Rivality Index

Ruiz, Irene Luque; Gómez‐Nieto, Miguel Ángel

doi:10.1021/acs.jcim.9b00264

Cited by 6 publications

(19 citation statements)

References 25 publications

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“…The efficiency of the RINH algorithm in the development of classification models has been described in previous works, , for balanced and imbalanced data sets and data sets of different sizes, obtaining results that are comparable with the most used machine learning algorithms such as support vector machine (SVM) or random forest.…”

Section: Introductionmentioning

confidence: 86%

“…In this section, a revisited study of the RINH (Rivality Index NeighborHood) algorithm has been carried out. This algorithm is able to generate robust and reliable classification models for binary data sets. , …”

Section: Methodsmentioning

confidence: 99%

“…In this paper, a prototype selection method to be applied in the preprocessing stage of the construction of the QSAR classification models using the Rivality Index NeighborHood (RINH) algorithm − is proposed. The RINH algorithm is based on the use of the rivality index.…”

Section: Introductionmentioning

confidence: 99%

“…Initially, instances which are considered border points are removed. The border points selected are the activity border molecules. , Finally, internal instances, those that do not contribute to the decision boundary, are also removed.…”

Section: Introductionmentioning

confidence: 99%

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Prototype Selection Method Based on the Rivality and Reliability Indexes for the Improvement of the Classification Models and External Predictions

Ruiz

Gómez‐Nieto

2020

J. Chem. Inf. Model.

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Prototype or instance selection techniques is an important field of research in knowledge discovery, data mining, and machine learning. In QSAR, the use of prototype selection techniques in the preprocessing stage of the construction of the QSAR models favors the data set curation, improving the interpretability and accuracy of the models as well as the performance of the algorithms. In this paper, we propose an efficient method for prototype selection to be used in the preprocessing stage of the construction of QSAR classification models. The proposed method is able to generate very high reduction rates in the cardinality of the training set while maintaining or even increasing the accuracy of the classification models. The validation of the method has been carried out by means of the prediction of external molecules, demonstrating that the prediction of new molecules is also maintained or even improved. The method has been tested using 40 benchmark data sets of different sizes and balancing ratios; the results of the tests have demonstrated the wide applicability domain of the proposed method.

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Section: Introductionmentioning

confidence: 86%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prototype Selection Method Based on the Rivality and Reliability Indexes for the Improvement of the Classification Models and External Predictions

Ruiz

Gómez‐Nieto

2020

J. Chem. Inf. Model.

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“…The two-dimensional quantitative structure-activity relationship (2D-QSAR) method has been applied to build prediction models of toxicity by determining the physical and chemical properties of chemical compounds from their chemical structures [29][30][31][32][33]. However, in conventional QSAR analysis, there are some problems concerning limited prediction performance [34][35][36][37]. Recently, QSAR analysis using the deep neural network (DNN) has shown superior prediction performance compared with other conventional machine learning (ML) methods [38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

et al. 2020

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The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.

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Improvement the performance of the classification models of Cyclooxygenase-2 inhibitors using undersampling methods based on the rivality and reliability indexes

Ruiz

Gómez‐Nieto

2020

J Math Chem

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Undersampling, prototype or instance selection techniques are oriented to remove redundant and noisy molecules from the datasets in order to reduce the computational cost and the necessity of memory in the construction of QSAR models, maintaining the performance of the models. In this paper, we describe and apply an undersampling technique based on the rivality and reliability indexes to the building of classification models for two Cyclooxinase-2 inhibitors datasets. In a preprocessing stage, the datasets are analyzed and curated and classification models are built using Support Vector Machine, Random Forest and Rivality Index Neighborhood algorithms. The results obtained clearly improve the ones described in the literature for these datasets for the training models and external validations carried out. Values of Matthews Correlation Coefficient higher than 0.9 for the training models and external validations proved the high robustness of the models generated using the undersampling technique capable of generating reductions of the datasets greater than 80%.

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Building of Robust and Interpretable QSAR Classification Models by Means of the Rivality Index

Cited by 6 publications

References 25 publications

Prototype Selection Method Based on the Rivality and Reliability Indexes for the Improvement of the Classification Models and External Predictions

Prototype Selection Method Based on the Rivality and Reliability Indexes for the Improvement of the Classification Models and External Predictions

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap–Deep Learning

Improvement the performance of the classification models of Cyclooxygenase-2 inhibitors using undersampling methods based on the rivality and reliability indexes

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