Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches

Belekar, Vilas; Lingineni, Karthik; Garg, Prabha

doi:10.2174/1386207318666150525094503

Cited by 17 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Schyman et al ( 2017 ) used the variable nearest neighbor (vNN) method to develop 15 ADMET prediction models and to use them to quickly assess some potential drug candidates, including toxicity, microsomal stability, mutagenicity, and likelihood of causing drug-induced liver injury. Belekar et al ( 2015 ) developed a computational model to identify compounds as breast cancer resistance protein (BCRP) inhibitors or not by using various machine learning approaches like SVM, k-NN, and the artificial neural network (ANN). The prediction accuracy of all three approaches was over 85%.…”

Section: In Silico Approachesmentioning

confidence: 99%

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Zhou

et al. 2020

Front. Chem.

210

View full text Add to dashboard Cite

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

show abstract

Section: In Silico Approachesmentioning

confidence: 99%

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Zhou

et al. 2020

Front. Chem.

210

View full text Add to dashboard Cite

show abstract

“…This algorithm is model agnostic and not relevant to the specific structure of a model. A couple of previous studies only identified and analyzed the important features given by feature selection procedure for QSAR models [21,29,106] but did not explore the relationships between features and different QSAR models. It is quite possible that a set of important descriptors/fingerprints for a well-performing QSAR model may be not important to another QSAR model.…”

Section: Interpretation Of Qsar Modelsmentioning

confidence: 99%

“…However, we found that some descriptors/fingerprints related to molecular lipophilicity (BCUT_ SLOGP_2, GCUT_SLOGP_0, and SlogP_VSA5), molecular surface area, volume and shape (std_dim2, glob, and vsurf_descriptors) and electronic properties (PEOE_VSA_ descriptors and Q_VSA_POS) were captured by most well-performing models. The importance of molecular lipophilicity descriptors in the development of QSAR models to distinguish BCRP inhibitors from non-inhibitors has been highlighted by the previous studies [8,15,21]. Moreover, according to the two-step mechanism for the inhibition of Fig.…”

Section: Comparison With Published Models and Interpretation Of Modelsmentioning

confidence: 99%

“…Quantitative structure-activity relationship (QSAR) analysis is one of the most classical techniques in the realm of computer-aided drug design (CADD) to correlate the chemical structures of molecules with their bioactivities. To date, numerous computational QSAR models based on various machine learning (ML) approaches and pharmacophore modeling have been proposed to predict BCRP inhibition [12][13][14][15][16][17][18][19][20][21][22] For example, in 2005, based on a dataset of 25 flavonoids, Zhang et al [15] built a multiple linear regression model to quantitatively predict the BCRP inhibition of flavonoids. In 2007, Matsson and co-workers used the orthogonal partial least-squares projection to latent structures discriminant analysis (OPLS-DA) to construct a classification model to differentiate BCRP inhibitors from non-inhibitors with predictive accuracies of 93% and 79% for the training and test sets, respectively [16].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, this model was not validated by a test set [20]. In 2015, based on a training set of 197 compounds, Belekar et al developed a number of models to distinguish BCRP inhibitors from non-inhibitors by using support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural networks (ANN), and the models achieved global accuracies of 0.828-0.878 for the test set with 99 compounds and 0.745-0.775 for the validation set with 99 compounds [21]. Recently, Montanari et al developed a computational model to qualitatively predict BCRP inhibition by using logistic regression (LR) based on a relatively large dataset with 978 compounds [22], and the tenfold crossvalidation of the LR model for the training set yielded a MCC of 0.65 and an AUC of 0.90.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning

et al. 2020

View full text Add to dashboard Cite

Breast cancer resistance protein (BCRP/ABCG2), an ATP-binding cassette (ABC) efflux transporter, plays a critical role in multi-drug resistance (MDR) to anti-cancer drugs and drug-drug interactions. The prediction of BCRP inhibition can facilitate evaluating potential drug resistance and drug-drug interactions in early stage of drug discovery. Here we reported a structurally diverse dataset consisting of 1098 BCRP inhibitors and 1701 non-inhibitors. Analysis of various physicochemical properties illustrates that BCRP inhibitors are more hydrophobic and aromatic than noninhibitors. We then developed a series of quantitative structure-activity relationship (QSAR) models to discriminate between BCRP inhibitors and non-inhibitors. The optimal feature subset was determined by a wrapper feature selection method named rfSA (simulated annealing algorithm coupled with random forest), and the classification models were established by using seven machine learning approaches based on the optimal feature subset, including a deep learning method, two ensemble learning methods, and four classical machine learning methods. The statistical results demonstrated that three methods, including support vector machine (SVM), deep neural networks (DNN) and extreme gradient boosting (XGBoost), outperformed the others, and the SVM classifier yielded the best predictions (MCC = 0.812 and AUC = 0.958 for the test set). Then, a perturbation-based model-agnostic method was used to interpret our models and analyze the representative features for different models. The application domain analysis demonstrated the prediction reliability of our models. Moreover, the important structural fragments related to BCRP inhibition were identified by the information gain (IG) method along with the frequency analysis. In conclusion, we believe that the classification models developed in this study can be regarded as simple and accurate tools to distinguish BCRP inhibitors from non-inhibitors in drug design and discovery pipelines.

show abstract

TRANSPORTERS‐MEDIATED DRUG DISPOSITION—PHYSIOCHEMISTRY AND IN SILICO APPROACHES

Varma¹,

El‐Kattan²,

Lai³

2022

Drug Transporters

View full text Add to dashboard Cite

Classification of Breast Cancer Resistant Protein (BCRP) Inhibitors and Non-Inhibitors Using Machine Learning Approaches

Cited by 17 publications

References 0 publications

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Computational Approaches in Preclinical Studies on Drug Discovery and Development

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning

TRANSPORTERS‐MEDIATED DRUG DISPOSITION—PHYSIOCHEMISTRY AND IN SILICO APPROACHES

Contact Info

Product

Resources

About