A classification model for blood brain barrier penetration

Singh, Manvi; Divakaran, Reshmi; Konda, Leela Sarath Kumar; Kristam, Rajendra

doi:10.1016/j.jmgm.2019.107516

Cited by 33 publications

(25 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…External estimates of probability that a compound will undergo active efflux mediated by P-glycoprotein (P-gp) can also be included [21,24,37]. In other approaches, large pools of various 1D, 2D (including molecular fingerprints), and 3D molecular descriptors calculated by different methods [26,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52] are analyzed by various statistical learning techniques, e.g., multiple linear regression, linear discriminant analysis, partial least squares regression, support vector machines, artificial neural networks, random forests, etc., often in combination with some descriptor selection protocols [23,24,26,[43][44][45][46][47][48][49][50][51][52]. However, the limited size of the training sets, use of unverified data, and too-small modeling errors for such an inherently noisy endpoint often give rise to the concerns of possible model overfitting [16].…”

Section: Introductionmentioning

confidence: 99%

Towards Deep Neural Network Models for the Prediction of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

2020

View full text Add to dashboard Cite

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (Q2=0.815, RMSEcv=0.318) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.

show abstract

Section: Introductionmentioning

confidence: 99%

Towards Deep Neural Network Models for the Prediction of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

2020

View full text Add to dashboard Cite

show abstract

“…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%

“…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]. While consensus models mitigate the over tting problem of single classi ers, they naturally require high computational power, especially when dealing with high dimensional data.…”

Section: Introductionmentioning

confidence: 99%

“…The features used to train these classi ers are often molecular descriptors which are chemical properties describing the drugs [3]. Some studies also add the ngerprints of the molecules in order to reach better prediction [8,9,12]. On the other hand, novel approaches apply the drug side effects and indications for BBB penetration prediction [14].…”

Section: Introductionmentioning

confidence: 99%

“…Since trying all possible combinations of feature vectors dramatically increases the required computational time and power, an effective feature selection algorithm is needed. In this context, D. Zhang et al [9] applied genetic algorithm (GA) for the selection of the appropriate features and optimization of SVM parameters. Nevertheless, choosing the most suitable algorithm for a given application is an important step since different algorithms may lead to convergence to different feature subsets and consequently affect the prediction results.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Saber¹,

Mhanna²,

Rihana³

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Blood–Brain Barrier Permeability Assessment for Small‐Molecule Drug Discovery Using Computational Techniques

Jing,

Xie

2023

Oral Bioavailability and Drug Delivery

View full text Add to dashboard Cite

A classification model for blood brain barrier penetration

Cited by 33 publications

References 31 publications

Towards Deep Neural Network Models for the Prediction of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

Towards Deep Neural Network Models for the Prediction of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

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

Blood–Brain Barrier Permeability Assessment for Small‐Molecule Drug Discovery Using Computational Techniques

Contact Info

Product

Resources

About