PgpRules: a decision tree based prediction server for P-glycoprotein substrates and inhibitors

Wang, Peihua; Tu, Yi-Shu; Tseng, Yufeng J.

doi:10.1093/bioinformatics/btz213

Cited by 26 publications

(15 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…P-gp substrates and inhibitors are usually tested in separate studies and naturally there are more studies with the focus on inhibitors [17,[66][67][68][69][70][71][72] instead of substrates [68,73]. The use of consensus modeling for this endpoint seems to be a viable option, a good example is the work of Yang et al [72].…”

Section: Permeability Glycoprotein (P-gp)mentioning

confidence: 99%

Machine learning models for classification tasks related to drug safety

et al. 2021

View full text Add to dashboard Cite

In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015–2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood–brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts. Graphical abstract

show abstract

Section: Permeability Glycoprotein (P-gp)mentioning

confidence: 99%

Machine learning models for classification tasks related to drug safety

et al. 2021

View full text Add to dashboard Cite

show abstract

“…With the advanced machine learning (ML) development, ML algorithms are used more often to construct CNS-QSAR models such as support vector machine (SVM) [ 18 , 19 ], decision tree (DT) [ 20 , 21 ] and random forest (RF) [ 22 , 23 ]. SVM is a non-probabilistic binary linear classifier, meaning it separates samples into one category or the other [ 7 , 11 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power

Battalora

et al. 2021

Briefings in Bioinformatics

View full text Add to dashboard Cite

The trade-off between a machine learning (ML) and deep learning (DL) model’s predictability and its interpretability has been a rising concern in central nervous system-related quantitative structure–activity relationship (CNS-QSAR) analysis. Many state-of-the-art predictive modeling failed to provide structural insights due to their black box-like nature. Lack of interpretability and further to provide easy simple rules would be challenging for CNS-QSAR models. To address these issues, we develop a protocol to combine the power of ML and DL to generate a set of simple rules that are easy to interpret with high prediction power. A data set of 940 market drugs (315 CNS-active, 625 CNS-inactive) with support vector machine and graph convolutional network algorithms were used. Individual ML/DL modeling methods were also constructed for comparison. The performance of these models was evaluated using an additional external dataset of 117 market drugs (42 CNS-active, 75 CNS-inactive). Fingerprint-split validation was adopted to ensure model stringency and generalizability. The resulting novel hybrid ensemble model outperformed other constituent traditional QSAR models with an accuracy of 0.96 and an F1 score of 0.95. With the power of the interpretability provided with this protocol, our model laid down a set of simple physicochemical rules to determine whether a compound can be a CNS drug using six sub-structural features. These rules displayed higher classification ability than classical guidelines, with higher specificity and more mechanistic insights than just for blood–brain barrier permeability. This hybrid protocol can potentially be used for other drug property predictions.

show abstract

“…The third step is to use ten-fold cross-validation to select the best classifier based on the reduced higher-level feature set. The candidate include the popular classifiers, such as SVM, Bayes ( Jahromi and Taheri, 2017 ), Decision Tree (DT) ( Wang et al, 2019 ), K-Nearest Neighbor (KNN) ( Wang et al, 2017 ), Random Forest (RF), Adaboost (Ada) and so on. In the fourth step, we test the effect of the proposed model on an independent test dataset, and compare its performance with other models.…”

Section: Methodsmentioning

confidence: 99%

i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification

et al. 2022

View full text Add to dashboard Cite

Parasites can cause enormous damage to their hosts. Studies have shown that antiparasitic peptides can inhibit the growth and development of parasites and even kill them. Because traditional biological methods to determine the activity of antiparasitic peptides are time-consuming and costly, a method for large-scale prediction of antiparasitic peptides is urgently needed. We propose a computational approach called i2APP that can efficiently identify APPs using a two-step machine learning (ML) framework. First, in order to solve the imbalance of positive and negative samples in the training set, a random under sampling method is used to generate a balanced training data set. Then, the physical and chemical features and terminus-based features are extracted, and the first classification is performed by Light Gradient Boosting Machine (LGBM) and Support Vector Machine (SVM) to obtain 264-dimensional higher level features. These features are selected by Maximal Information Coefficient (MIC) and the features with the big MIC values are retained. Finally, the SVM algorithm is used for the second classification in the optimized feature space. Thus the prediction model i2APP is fully constructed. On independent datasets, the accuracy and AUC of i2APP are 0.913 and 0.935, respectively, which are better than the state-of-arts methods. The key idea of the proposed method is that multi-level features are extracted from peptide sequences and the higher-level features can distinguish well the APPs and non-APPs.

show abstract

PgpRules: a decision tree based prediction server for P-glycoprotein substrates and inhibitors

Cited by 26 publications

References 16 publications

Machine learning models for classification tasks related to drug safety

Machine learning models for classification tasks related to drug safety

Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power

i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification

Contact Info

Product

Resources

About