Computer modeling of blood brain barrier permeability of physiologically active compounds

Raevsky, Oleg A.; Солодова, С. Л.; Lagunin, Alexey; Poroikov, Vladimir

doi:10.18097/pbmc20146002161

Cited by 14 publications

(4 citation statements)

References 119 publications

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“…Raevsky et al. used a method called “read‐across” to study the structure–activity relationship of the BBB, and the method showed that the hydrogen bond donor–acceptor would be the main factor of penetration . Also, Martins et al.…”

Section: Introductionmentioning

confidence: 99%

In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

et al. 2018

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The blood-brain barrier (BBB) as a part of absorption protects the central nervous system by separating the brain tissue from the bloodstream. In recent years, BBB permeability has become a critical issue in chemical ADMET prediction, but almost all models were built using imbalanced data sets, which caused a high false-positive rate. Therefore, we tried to solve the problem of biased data sets and built a reliable classification model with 2358 compounds. Machine learning and resampling methods were used simultaneously for the refinement of models with both 2 D molecular descriptors and molecular fingerprints to represent the chemicals. Through a series of evaluation, we realized that resampling methods such as Synthetic Minority Oversampling Technique (SMOTE) and SMOTE+edited nearest neighbor could effectively solve the problem of imbalanced data sets and that MACCS fingerprint combined with support vector machine performed the best. After the final construction of a consensus model, the overall accuracy rate was increased to 0.966 for the final external data set. Also, the accuracy rate of the model for the test set was 0.919, with an excellent balanced capacity of 0.925 (sensitivity) to predict BBB-positive compounds and of 0.899 (specificity) to predict BBB-negative compounds. Compared with other BBB classification models, our models reduced the rate of false positives and were more robust in prediction of BBB-positive as well as BBB-negative compounds, which would be quite helpful in early drug discovery.

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Section: Introductionmentioning

confidence: 99%

In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

et al. 2018

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“…В работе использованы тщательно отобранные данные logBB в равновесном состоянии. Объектом тестирования были крысы [18].…”

Section: материалы и методыunclassified

Binary classification of blood-brain barrier penetration by the logistic regression method

Raevsky

Polianczyk

Raevskaja

2018

BMCRM

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“…Because P-gp participates in the development of resistance to not only antibiotics and cytostatics but also many drugs that are either its substrates or even inductors, the analysis of polymorphism in the genes of P-gp and other transporters involved in MDR, like the search for overcoming resistance, is assuming a larger role. For this, the use of computational technology to design inhibitors for targeted chemical synthesis was also proposed [ 11 ].…”

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confidence: 99%

Pharmacological Strategies for Overcoming Multidrug Resistance to Chemotherapy

Федотчева

Shimanovsky

2023

Pharm Chem J

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Actual mechanisms of multidrug resistance (MDR) to chemotherapy in oncology are considered. ABC-transporters such as P-glycoprotein, BCRP protein, and MRP proteins take part in the development of resistance. The review presents the main classes of chemosensitizers, i.e., inhibitors of ABC transporters of the 1 st -4 th generations. Plant polyphenols, i.e., flavonoids, are commonly referred to as the last (4 th ) generation of MDR inhibitors. Chemosensitizers of different classes should be chosen with allowance for the patient mutation-expression profile and the receptor status of a particular tumor. The appropriate dosage of the chemosensitizer and the administration schedule can enhance the process of counteracting MDR.

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Computer modeling of blood brain barrier permeability of physiologically active compounds

Cited by 14 publications

References 119 publications

In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

Binary classification of blood-brain barrier penetration by the logistic regression method

Pharmacological Strategies for Overcoming Multidrug Resistance to Chemotherapy

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