Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set
In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is ad-dressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledg...
Saved in:
Main Author: | |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2016
|
Subjects: | |
Online Access: | http://repository.vnu.edu.vn/handle/VNU_123/11508 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Vietnam National University, Hanoi |
Language: | English |
id |
oai:112.137.131.14:VNU_123-11508 |
---|---|
record_format |
dspace |
spelling |
oai:112.137.131.14:VNU_123-115082017-04-05T14:27:40Z Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set Le, Thi Thu Huong Linear discriminant analysis Multiple linear regression relationship · Blood brain barrier BBB endpoint Dragon descriptor In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is ad-dressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in terms of log BB. Prior to the model development, steps of data analysis that comprise chemical data curation, structural, cutoff and cluster analy-sis (CA) were conducted. Linear Discriminant Analysis (LDA) and Multiple Linear Regression (MLR) were used to fit clas-sification and correlation functions. The best LDA-based model showed overall accuracies over 85% and 83% and for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than 69% of the variance in the experimental logBBwas developed. A brief and general interpretation of proposed models allowed the estimation on how ‘near’ our computa-tional approach is to the factors that determine the pas-sage of molecules through the BBB. In a final effort some popular and powerful Machine Learning methods were considered. Comparable or quite similar performance was observed respect to the simpler linear techniques. Most of the compounds with anomalous behavior were put aside into a set denoted as controversial set and discussion re-garding to several compounds is provided. Finally, our re-sults were compared with methodologies previously report-ed in the literature showing comparable to better results. The results could represent useful tools available and repro-ducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects. 2016-05-30T17:52:37Z 2016-05-30T17:52:37Z 2015 Article 1868-1751 http://repository.vnu.edu.vn/handle/VNU_123/11508 en application/pdf Wiley |
institution |
Vietnam National University, Hanoi |
building |
VNU Library & Information Center |
country |
Vietnam |
collection |
VNU Digital Repository |
language |
English |
topic |
Linear discriminant analysis Multiple linear regression relationship · Blood brain barrier BBB endpoint Dragon descriptor |
spellingShingle |
Linear discriminant analysis Multiple linear regression relationship · Blood brain barrier BBB endpoint Dragon descriptor Le, Thi Thu Huong Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set |
description |
In the present report, the challenging task of
drug delivery across the blood-brain barrier (BBB) is ad-dressed via a computational approach. The BBB passage
was modeled using classification and regression schemes
on a novel extensive and curated data set (the largest to
the best of our knowledge) in terms of log BB. Prior to the
model development, steps of data analysis that comprise
chemical data curation, structural, cutoff and cluster analy-sis (CA) were conducted. Linear Discriminant Analysis (LDA)
and Multiple Linear Regression (MLR) were used to fit clas-sification and correlation functions. The best LDA-based
model showed overall accuracies over 85% and 83% and
for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than
69% of the variance in the experimental logBBwas developed. A brief and general interpretation of proposed
models allowed the estimation on how ‘near’ our computa-tional approach is to the factors that determine the pas-sage of molecules through the BBB. In a final effort some
popular and powerful Machine Learning methods were
considered. Comparable or quite similar performance was
observed respect to the simpler linear techniques. Most of
the compounds with anomalous behavior were put aside
into a set denoted as controversial set and discussion re-garding to several compounds is provided. Finally, our re-sults were compared with methodologies previously report-ed in the literature showing comparable to better results.
The results could represent useful tools available and repro-ducible by all scientific community in the early stages of
neuropharmaceutical drug discovery/development projects. |
format |
Article |
author |
Le, Thi Thu Huong |
author_facet |
Le, Thi Thu Huong |
author_sort |
Le, Thi Thu Huong |
title |
Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set |
title_short |
Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set |
title_full |
Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set |
title_fullStr |
Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set |
title_full_unstemmed |
Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set |
title_sort |
towards better bbb passage prediction using an extensive and curated data set |
publisher |
Wiley |
publishDate |
2016 |
url |
http://repository.vnu.edu.vn/handle/VNU_123/11508 |
_version_ |
1680965444599545856 |