Effect of Selection of Molecular Descriptors on the Prediction of Blood−Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods

2005 ◽  
Vol 45 (5) ◽  
pp. 1376-1384 ◽  
Author(s):  
Hu Li ◽  
Chun Wei Yap ◽  
Choong Yong Ung ◽  
Ying Xue ◽  
Zhi Wei Cao ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Blood Brain Barrier ◽  
Molecular Descriptors ◽  
Brain Barrier ◽  
Learning Methods ◽  
Blood Brain ◽  
Selection Of

scholarly journals Prediction of Blood-Brain Barrier Penetration (BBBP) Based on Molecular Descriptors of the Free-Form and In-Blood-Form Datasets

Molecules ◽  
2021 ◽  
Vol 26 (24) ◽  
pp. 7428
Author(s):  
Hiroshi Sakiyama ◽  
Motohisa Fukuda ◽  
Takashi Okuno
Keyword(s):  
Blood Brain Barrier ◽  
Molecular Descriptors ◽  
Predictive Performance ◽  
Comparable Result ◽  
Brain Barrier ◽  
Free Form ◽  
Reliable Prediction ◽  
Blood Brain ◽  
Barrier Penetration ◽  
The Brain

The blood-brain barrier (BBB) controls the entry of chemicals from the blood to the brain. Since brain drugs need to penetrate the BBB, rapid and reliable prediction of BBB penetration (BBBP) is helpful for drug development. In this study, free-form and in-blood-form datasets were prepared by modifying the original BBBP dataset, and the effects of the data modification were investigated. For each dataset, molecular descriptors were generated and used for BBBP prediction by machine learning (ML). For ML, the dataset was split into training, validation, and test data by the scaffold split algorithm MoleculeNet used. This creates an unbalanced split and makes the prediction difficult; however, we decided to use that algorithm to evaluate the predictive performance for unknown compounds dissimilar to existing ones. The highest prediction score was obtained by the random forest model using 212 descriptors from the free-form dataset, and this score was higher than the existing best score using the same split algorithm without using any external database. Furthermore, using a deep neural network, a comparable result was obtained with only 11 descriptors from the free-form dataset, and the resulting descriptors suggested the importance of recognizing the glucose-like characteristics in BBBP prediction.

scholarly journals Why are neurotransmitters neurotoxic? An evolutionary perspective

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 179 ◽  
Author(s):  
Keith D. Harris ◽  
Meital Weiss ◽  
Amotz Zahavi
Keyword(s):  
Neurodegenerative Diseases ◽  
Blood Brain Barrier ◽  
Specific Region ◽  
Brain Barrier ◽  
Evolutionary Perspective ◽  
Blood Brain ◽  
Additional Role ◽  
Selection Of

In the CNS, minor changes in the concentration of neurotransmitters such as glutamate or dopamine can lead to neurodegenerative diseases. We present an evolutionary perspective on the function of neurotransmitter toxicity in the CNS. We hypothesize that neurotransmitters are selected because of their toxicity, which serves as a test of neuron quality and facilitates the selection of neuronal pathways. This perspective may offer additional explanations for the reduction of neurotransmitter concentration in the CNS with age, and suggest an additional role for the blood-brain barrier. It may also suggest a connection between the specific toxicity of the neurotransmitters released in a specific region of the CNS, and elucidate their role as chemicals that are optimal for testing the quality of cells in that region.

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