Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine

Author(s): Hang Li, Maolin Wang, Ya-Nan Gong, Aixia Yan

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 19 , Issue 6 , 2016

Become EABM
Become Reviewer
Call for Editor


β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer's disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inhibitors using Kohonen’s Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular descriptor was calculated using the program ADRIANA.Code. We adopted two different methods: random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%, 0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98, respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be obtained from the authors by email or other contacts.

Keywords: Classification models, BACE1 inhibitor, Kohonen’s self-organizing map (SOM), support vector machine (SVM).

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2016
Published on: 08 June, 2016
Page: [470 - 480]
Pages: 11
DOI: 10.2174/1386207319666160504095621
Price: $65

Article Metrics

PDF: 25
PRC: 1