Development of Machine Learning Based Blood-brain Barrier Permeability Prediction Models Using Physicochemical Properties, MACCS and Substructure Fingerprints

Saxena, Deeksha; Sharma, Anju; Siddiqui, Mohammed Haris; Rajnish, Kumar

doi:10.2174/1574893616666210203104013

Cited by 9 publications

(8 citation statements)

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“…SVMProt-188D is a feature extraction method based on the amino acid composition and physicochemical properties ( Dubchak et al, 1995 ; Saxena et al, 2021 ). It encodes each protein sequence as a 188-dimensional feature vector.…”

Section: Methodsmentioning

confidence: 99%

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

Chen

2022

Front. Genet.

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The major histocompatibility complex (MHC) is a large locus on vertebrate DNA that contains a tightly linked set of polymorphic genes encoding cell surface proteins essential for the adaptive immune system. The groups of proteins encoded in the MHC play an important role in the adaptive immune system. Therefore, the accurate identification of the MHC is necessary to understand its role in the adaptive immune system. An effective predictor called PredMHC is established in this study to identify the MHC from protein sequences. Firstly, PredMHC encoded a protein sequence with mixed features including 188D, APAAC, KSCTriad, CKSAAGP, and PAAC. Secondly, three classifiers including SGD, SMO, and random forest were trained on the mixed features of the protein sequence. Finally, the prediction result was obtained by the voting of the three classifiers. The experimental results of the 10-fold cross-validation test in the training dataset showed that PredMHC can obtain 91.69% accuracy. Experimental results on comparison with other features, classifiers, and existing methods showed the effectiveness of PredMHC in predicting the MHC.

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Section: Methodsmentioning

confidence: 99%

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

Chen

2022

Front. Genet.

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“…Therefore, alternative methods with lower costs such as computational models are needed for drug development and research. Nowadays, due to the high-speed development of computational technology, various in silico prediction models to evaluate compound ADME properties have been developed, − also for BBB transfer − (shown in Table S1).…”

Section: Introductionmentioning

confidence: 99%

Explaining Blood–Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms

Cornelissen,

Markert,

Deutsch

et al. 2023

J. Med. Chem.

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The blood–brain barrier (BBB) represents a major obstacle to delivering drugs to the central nervous system (CNS), resulting in the lack of effective treatment for many CNS diseases including brain cancer. To accelerate CNS drug development, computational prediction models could save the time and effort needed for experimental evaluation. Here, we studied BBB permeability focusing on active transport (influx and efflux) as well as passive diffusion using previously published and self-curated data sets. We created prediction models based on physicochemical properties, molecular substructures, or their combination to understand which mechanisms contribute to BBB permeability. Our results show that features that predicted passive diffusion over membranes overlap with features that explain endothelial permeation of approved CNS-active drugs. We also identified physical properties and molecular substructures that positively or negatively predicted BBB transport. These findings provide guidance toward identifying BBB-permeable compounds by optimally matching physicochemical and molecular properties to BBB transport mechanisms.

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“…Upon validation of the developed model using 1,566 compounds, the prediction accuracy was found to be 86.5% only. Very recently, Saxena et al (2021) proposed an ML-based BBB permeability prediction model using 1,978 compounds ( Saxena et al, 2021 ). The study group found that SVM with the RBF kernel yielded an accuracy of 96.77% with AUC and F1 score values of 0.964 and 0.975, respectively, which outperformed the kNN, random forest, and naïve Bayes algorithms in the prediction of BBB permeability on the same dataset.…”

Section: Introductionmentioning

confidence: 99%

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

et al. 2022

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The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains homeostasis inside the central nervous system (CNS). The BBB permeability of compounds is an important consideration during CNS-acting drug development and is difficult to formulate in a succinct manner. Clinical experiments are the most accurate method of measuring BBB permeability. However, they are time taking and labor-intensive. Therefore, numerous efforts have been made to predict the BBB permeability of compounds using computational methods. However, the accuracy of BBB permeability prediction models has always been an issue. To improve the accuracy of the BBB permeability prediction, we applied deep learning and machine learning algorithms to a dataset of 3,605 diverse compounds. Each compound was encoded with 1,917 features containing 1,444 physicochemical (1D and 2D) properties, 166 molecular access system fingerprints (MACCS), and 307 substructure fingerprints. The prediction performance metrics of the developed models were compared and analyzed. The prediction accuracy of the deep neural network (DNN), one-dimensional convolutional neural network, and convolutional neural network by transfer learning was found to be 98.07, 97.44, and 97.61%, respectively. The best performing DNN-based model was selected for the development of the “DeePred-BBB” model, which can predict the BBB permeability of compounds using their simplified molecular input line entry system (SMILES) notations. It could be useful in the screening of compounds based on their BBB permeability at the preliminary stages of drug development. The DeePred-BBB is made available at https://github.com/12rajnish/DeePred-BBB.

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Development of Machine Learning Based Blood-brain Barrier Permeability Prediction Models Using Physicochemical Properties, MACCS and Substructure Fingerprints

Cited by 9 publications

References 0 publications

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

Explaining Blood–Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

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