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.
| Title: | Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set |
| Authors: | Le, Thi Thu Huong |
| Keywords: | Linear discriminant analysis, Multiple linear regression relationship, Blood brain barrier, BBB endpoint, Dragon descriptor |
| Issue Date: | 2015 |
| Publisher: | Wiley |
| Abstract: | 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. |
| URI: | http://repository.vnu.edu.vn/handle/VNU_123/11508 |
| ISSN: | 1868-1751 |
| Appears in Collections: | SMP - Papers / Tham luận HN-HT |
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