A Simple Method to Predict Blood-Brain Barrier Permeability of Drug- Like Compounds Using Classification Trees

Castillo-Garit, Juan A.; Casañola-Martín, Gerardo M.; Le-Thi-Thu, Huong; Pham-The, Hai; Barigye, Stephen J.

doi:10.2174/1573406413666170209124302

Cited by 29 publications

(24 citation statements)

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“…The fpr values behaved below the 12% threshold [LDA (α‐amylase): 3.67% training, 9.17% cross‐validation, and 11.82% for the test sets] and for the case of LDA (α‐glucosidase): 2.73% training, 11.82% cross‐validation, and 10.88% test sets. Although these values are higher than the previous case, they keep below 15%, which is also recommended (Castillo‐Garit, Casanola‐Martin, Le‐Thi‐Thu, Pham‐The, & Barigye, ).…”

Section: Resultsmentioning

confidence: 88%

Beyond model interpretability using LDA and decision trees for α‐amylase and α‐glucosidase inhibitor classification studies

et al. 2019

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In this report are used two data sets involving the main antidiabetic enzyme targets α‐amylase and α‐glucosidase. The prediction of α‐amylase and α‐glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α‐amylase and 1546 compounds in the case of α‐glucosidase are selected to develop the tree model. In the case of CT‐J48 have the better classification model performances for both targets with values above 80%–90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10‐fold cross‐validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double‐target approach for increasing the estimation of antidiabetic chemicals identification aimed by double‐way workflow in virtual screening pipelines.

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Section: Resultsmentioning

confidence: 88%

Beyond model interpretability using LDA and decision trees for α‐amylase and α‐glucosidase inhibitor classification studies

et al. 2019

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“…Additionally, based on 3PRule and models developed by Pham‐The et al ., all compounds were classified as moderate permeability with P app values ranged from 2.8 to 7.2 × 10 −6 cm/s. Using the BOILED‐Egg approach in consensus with our recent methods, all compounds were estimated as having high gastrointestinal (GI) absorption and good ability to permeant through Blood‐Brain Barrier (BBB). However, 4b , 4c , and 4g were identified as potential P‐glycoprotein substrate .…”

Section: Resultsmentioning

confidence: 99%

Quinazoline‐Based Hydroxamic Acids: Design, Synthesis, and Evaluation of Histone Deacetylase Inhibitory Effects and Cytotoxicity

Hieu

Anh

Pham-The

et al. 2018

Chemistry & Biodiversity

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In our search for novel histone deacetylases inhibitors, we have designed and synthesized a series of novel hydroxamic acids and N-hydroxybenzamides incorporating quinazoline heterocycles (4a - 4i, 6a - 6i). Bioevaluation showed that these quinazoline-based hydroxamic acids and N-hydroxybenzamides were potently cytotoxic against three human cancer cell lines (SW620, colon; PC-3, prostate; NCI-H23, lung). In term of cytotoxicity, several compounds, e.g., 4g, 4c, 4g - 4i, 6c, and 6h, displayed from 5- up to 10-fold higher potency than SAHA (suberoylanilidehydroxamic acid, vorinostat). The compounds were also generally comparable to SAHA in inhibiting HDACs with IC values in sub-micromolar range. Some compounds, e.g., 4g, 6c, 6e, and 6h, were even more potent HDAC inhibitors compared to SAHA in HeLa extract assay. Docking studies demonstrated that the compounds tightly bound to HDAC2 at the active binding site with binding affinities higher than that of SAHA. Detailed investigation on the estimation of absorption, distribution, metabolism, excretion, and toxicity (ADMET) suggested that compounds 4g, 6c, and 6g, while showing potent HDAC2 inhibitory activity and cytotoxicity, also potentially displayed ADMET characteristics desirable to be expected as promising anticancer drug candidates.

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“…In fact, this descriptor appeared in the best four RF models created in [5] using up to four descriptors. Moreover, PSA was used in a classi cation tree model [7] as the criterion allowing the rst split of the tree. In addition, D. Zhang et al [11] listed PSA among the ten most signi cant descriptors.…”

Section: Discussionmentioning

confidence: 99%

“…While such models assign speci c logBB/PR values for each drug, binary models have so far reached a higher prediction accuracy and provide a preliminary insight regarding the behavior of candidate drugs which is su cient in early drug discovery stages. Predominantly, binarization of drug permeability across the BBB is performed by setting empirical thresholds to logBB values [5][6][7][8][9]. However, S. Kunwittaya et al [6] have shown that varying logBB thresholds lead to a difference in the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning is ubiquitously applied in the case of binary BBB models. In this context, different types of classi ers were trained in the literature including Support Vector Machines (SVM) [6,8,11,12], Linear Discriminant Analysis (LDA) [13], Arti cial Neural Networks (ANN) [6] and Multi-Layer Perceptron (MLP) [8,9], k-Nearest Neighbors (k-NN) [8], Decision Trees (DT) [6,7] and Random Forests (RF) [5,8,9]. Other studies apply consensus models, by training and combining multiple classi ers [8,9].…”

Section: Introductionmentioning

confidence: 99%

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A machine learning model for the prediction of drug permeability across the Blood-Brain Barrier: a comparative approach

Saber¹,

Mhanna²,

Rihana³

2020

Preprint

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Background: Drug permeability across the blood-brain barrier (BBB) is a critical challenge for successful drug discovery which has led to multiple efforts to develop in silico predictive models. Most of the in silico models are based on the molecular descriptors of the drugs. In this work, we compare the ability of sequential feature selection and genetic algorithms in selecting the most relevant descriptors and hence enhancing the permeability prediction accuracy.Methods: Five different classifiers were initially trained on a dataset using eight molecular descriptors. Then, sequential feature selection and genetic algorithms were performed separately and the same classifiers were trained using the descriptors chosen by each algorithm.Results: The highest overall accuracy obtained without feature selection was 94.98%. This accuracy increased with sequential feature selection and genetic algorithms on multiple classifiers. However, the highest accuracy (96.23%) was obtained after performing genetic algorithm on the feature vector. Moreover, genetic algorithm with a fitness function based on the performance of a support vector machine led to an increase in the accuracy of all the tested classifiers unlike sequential feature selection.Conclusions: The findings show that genetic algorithm is a more robust approach than sequential feature selection in choosing the most relevant molecular descriptors involved in the permeability across the blood-brain barrier. The results also highlight the importance of the polar surface area of drugs in crossing the BBB.

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A Simple Method to Predict Blood-Brain Barrier Permeability of Drug- Like Compounds Using Classification Trees

Abstract: Finally, we can say that, the present model would be a valuable tool in the early stages of drug discovery process of neuropharmaceuticals.

Cited by 29 publications

References 0 publications

Beyond model interpretability using LDA and decision trees for α‐amylase and α‐glucosidase inhibitor classification studies

Beyond model interpretability using LDA and decision trees for α‐amylase and α‐glucosidase inhibitor classification studies

Quinazoline‐Based Hydroxamic Acids: Design, Synthesis, and Evaluation of Histone Deacetylase Inhibitory Effects and Cytotoxicity

A machine learning model for the prediction of drug permeability across the Blood-Brain Barrier: a comparative approach

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