In silico and in vitro Blood-Brain Barrier models for early stage drug discovery

Saber, Ralph; Rihana, Sandy; Mhanna, Rami

doi:10.1109/icabme47164.2019.8940222

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…The author concludes that the deep learning method outperforms other ML algorithms in terms of area under curve, accuracy, and F1-score i.e., 98%, 97%, and 92% respectively. Saber et al 2019 [22] implement SVM, ANN, and KNN models with 1593 drug compounds. For the selection of molecular features, a genetic algorithm was used which generated 8 descriptors.…”

Section: Related Workmentioning

confidence: 99%

A Computational Prediction Model of Blood-Brain Barrier Penetration Based on Machine Learning Approaches

Ajabani

2023

IJACSA

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Within the field of medical sciences, addressing brain illnesses such as Alzheimer's disease, Parkinson's disease, and brain tumors poses significant difficulties. Despite thorough investigation, the search for truly successful neurotherapies continues to be challenging to achieve. The blood-brain barrier (BBB), which is currently a major area of research, restricts the passage of medicinal substances into the central nervous system (CNS). It is crucial in the field of neuroscience to create drugs that can effectively cross the blood-brain barrier (BBB) and treat cognitive disorders. The objective of this study is to improve the accuracy of machine learning models in predicting BBB permeability, which is a critical factor in medication development. In recent times, a range of machine learning models such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Artificial Neural Networks (ANN), and Random Forests (RF) have been utilized for BBB. By employing descriptors of varying dimensions (1D, 2D, or 3D), these models demonstrate the potential to make precise predictions. However, the majority of these studies are biased to the nature of datasets. To accomplish our objective, we utilized three BBB datasets for training and testing our model. The Random Forest (RF) model has shown exceptional performance when used on larger datasets and extensive feature sets. The RF model attained an overall accuracy of 90.36% with 10-fold cross-validation. Additionally, it earned an AUC of 0.96, a sensitivity of 77.73%, and a specificity of 94.74%. The assessment of an external dataset resulted in an accuracy rate of 91.89%, an AUC value of 0.94, a sensitivity rate of 91.43%, and a specificity rate of 92.31%.

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Section: Related Workmentioning

confidence: 99%

A Computational Prediction Model of Blood-Brain Barrier Penetration Based on Machine Learning Approaches

Ajabani

2023

IJACSA

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Neuroinformatics Insights towards Multiple Neurosyphilis Complications

et al. 2022

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Treponema pallidum subspecies pallidum causes syphilis, a sexually transmitted disease that infects more than 2.1 million pregnant women every year. Due to its maximum death rates and augmented risk of human immunodeficiency virus (HIV) infection, the disease is still a matter of debate in many low- and high-income countries. The infection has three stages that lead to several complications if left untreated and can lead to many tertiary complications in the brain, eyes, ears, heart, and pregnancy. Neurosyphilis is also known as the clinical result of infection of the central nervous system by Treponema pallidum subspecies pallidum. It can evolve at any time and from any stage of syphilis exposure. This review briefly explains the severe and multiple neurosyphilitic complications and recently identified cases related to neurosyphilis. We also explained computational neuroscience, neuroinformatics, and in silico models and techniques based on artificial intelligence and other computational and mathematical methods. These techniques have already been applied to several neurological and psychological brain complications and can be applied to neurosyphilis to better understand the persistence of the disease related to the brain that causes neurosyphilis.

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In silico and in vitro Blood-Brain Barrier models for early stage drug discovery

Cited by 2 publications

References 6 publications

A Computational Prediction Model of Blood-Brain Barrier Penetration Based on Machine Learning Approaches

A Computational Prediction Model of Blood-Brain Barrier Penetration Based on Machine Learning Approaches

Neuroinformatics Insights towards Multiple Neurosyphilis Complications

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