http://repository.vnu.edu.vn/handle/VNU_123/11508
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.
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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